Students in Physical Sciences are often well versed in the art of model building but less so in the process of model-selection when multiple models can describe the same data. Students rarely learn tools beyond curve fitting and least square error minimization for model selection. Consequently, students are often unaware of the scope of different tools and fail to make judicious choice of algorithms/theories when faced with diverse problems. For example, building a model from data is very different from generating data (stochastic or deterministic) from a model. Next consider two contrasting challenges of model building i) when there is limited data vs ii) when there is too much data. For the first problem -- inferring models from limited data -- the solution can be traced back to Boltzmann's formulation of Statistical Physics describing motion of atoms. The connection between Information theory, Inference and Boltzmann's description, however, is often overlooked in introductory or even advanced classes in Physics, and Statistics. Studying these similarities can unlock novel solutions for problems well outside of thermodynamics, even as far as Image processing, Biology and Network science. Inference also requires us to appreciate fundamental topics in Probability -- difference between frequentist and non-frequentist approach, Bayesian formalism -- that are rarely taught to physical scientists, life scientists or engineers. At the other extreme, faced with data deluge, we routinely ask: how do we make sense of too much data ? We use clustering, PCA, Neural Networks. In this course we will discuss and connect all these seemingly disparate concepts and apply them -- at the appropriate context -- to diverse problems in Physics, Chemistry, Biology and beyond. In the process we will gain an in-depth knowledge about commonly heard but perhaps less understood topics such as: Entropy, Likelihood maximization, Bayesian statistics, PCA, Classification algorithms, and Neural Networks. We will also address another often overlooked but fundamental and fascinating topic, biology's inherent ability to encode and decode information. Currently there is no such course that address all these topics in Information and Data Science in an unified manner -- deeply connecting their formal basis, regime of applicability -- grounded on physical principles, with a forward looking approach towards application in many areas well outside of traditional sciences. A lot of learning in the course will happen `on the fly', where the tools and application problems are learnt as needed.
INFO 4120 Python Programming (4 Credits)
Python is a popular general purpose programming language which is well suited to a wide range of problems. With the right set of add-ons, it is comparable to domain-specific languages such as R and MATLAB. Python is a scripting language. The following topics will be covered: Importing data, Reading and writing files, Cleaning and Managing Data, Merging and joining DataFrame objects, Plotting and Visualization, Statistical Analysis, Fitting data to probability distributions and Linear models. Packages: Pandas, NumPy, matplotlib, statsmodels, Scikit-learn, and IPython. Principal Content Elements: 1. Introduction to Programming Logic and Design Using Python 2. Data Management 3. Statistical Analysis 4. Advanced Data Management and Statistical Analysis Prerequisites: STAT 4610.
ENGR 4622 Advanced Optimization (4 Credits)
Optimization is an indispensable tool for many fields of science and engineering and is one of the pillars of data science and machine learning. This course introduces optimization methods that are suitable for large-scale problems arising in data science, machine learning, and other engineering applications. We will discuss the development, computation, and convergence aspects for algorithms including gradient methods, accelerated methods, quasi-Newton methods, stochastic optimization, variance reduction, online optimization, as well as distributed optimization. We will also exploit the efficacy of these methods in concrete data science problems, including learning low-dimensional models, deep learning, and (possible) reinforcement learning. This course together with ENGR 4620 Optimization will provide in-depth introductions to optimization.
INTS 4059 Data Science With Python (4 Credits)
Python is a widely used programming language for data exploration. In this course, students will first learn programming concepts like data types, regular expressions, conditional statements, loops, functions, and so forth. They will then learn how to write Python programs to conduct data exploration, statistical analysis, visualization, and predictive analysis techniques like decision trees and text mining. Students will also learn how to use various libraries available in Python (like Numpy, Pandas etc.) in their programs. Finally, they will learn how to read and debug (i.e. fix) Python programs written by someone else. No prior programming experience is necessary to enroll in this course.
COMP 3006 Python Software Development (4 Credits)
This accelerated course covers advanced Pythonprogramming for data scientists and cybersecurity professionals. Course Objectives: name and demonstrate proficiency using advanced Pythonprogramming techniques; analyze a programming task and create a development plan and high-level software design that accomplishes the task; relate common portions of the Python standard library to specific programming tasks; understand and apply aspects of the Python scientific programming ecosystem to achieve an analysis goal. Prerequisite: COMP 3005.
INFO 4250 Business Data and Analytics (4 Credits)
Businesses make decisions and improve processes using their own and external data with a variety of data-driven and analytic techniques. This course introduces students to the business data landscape, data management in commercial organizations, and the data-driven decision-making process. Students explore the fundamental concepts behind how data and analytics can improve business performance, using their individual roles and companies as subject matter. Principal Content Elements: 1. Data-driven decision making and performance improvement. 2. Data management in organizations. 3.Hands-on experience creating visualizations for data-driven insights.
INFO 4250 requires a Windows Operating System. MAC users will have to virtualize their machines, or have access to a PC for async, live session and graded assignments. The software used in this course is Power BI and Visio Pro, which are Windows-only applications. Power BI is free, and the Department of Business Information and Analytics will provide a license for Visio Pro.
ACTG 4176 Accounting Data Analytics (4 Credits)
In this course, students explore overarching trends in big data and the impact to accounting and auditing fields while also gaining hands on experience working with business data sets. In today’s information world, accountants must be well equipped to understand and utilize the vast and varying data systems that feed a company’s decision making process. This course allows students to develop big data skills by learning the SQL language to query data from mock clients. Students execute Computer Assisted Auditing Techniques (CAATs) using both the SQL language as well was the audit data mining tool, IDEA. Students simulate the process to request client data files, load complex data sets, design and execute query procedures and summarize results for management. Prerequisite: ACTG 4610.
INTS 4062 Data Science with R (4 Credits)
R is a widely used programming language for data analysis. In this course, students will first learn programming concepts like data types, regular expressions, conditional statements, loops, functions etc. They will then learn how to write R programs to conduct data exploration, visualization, basic statistical analyses, as well as produce reproducible reports. Students will also learn how to use various packages available in R in their programs. Finally, they will learn how to read and debug (i.e. fix) R programs written by someone else. No prior programming experience is necessary to enroll in this course.
PSYC 3029 Imaging the Mind (4 Credits)
Imaging the Mind is an introductory course to the basic theory and data analysis techniques used in functional magnetic resonance imaging (fMRI). It will cover basic brain anatomy, the basic physics of MRI, experimental design, data processing and the issues associated with data processing, and interpretation of fMRI data. Students in this course will receive hands-on experience in processing a data set from start to finish. They will apply different image preprocessing techniques, statistical design parameters, and statistical models to determine how these factors influence the outcome of the data and how these factors influence the interpretation of that data. In this manner, each student will be exposed individually to the decision issues and interpretation pitfalls involved in fMRI data analysis. Cross listed with PSYC 4255. Prerequisites: PSYC 2031 and PSYC 3050, must be major or minor in psychology, must have junior standing. Permission of the instructor required.
PSYC 4255 Imaging the Mind (4 Credits)
Imaging Cognition is an introductory course to the basic theory and data analysis techniques used in functional magnetic resonance imaging (fMRI). It will cover basic brain anatomy, the basic physics of MRI, experimental design, data processing and the issues associated with data processing, and interpretation of fMRI data. Students in this course will receive hands-on experience in processing a data set from start to finish. They will apply different image preprocessing techniques, statistical design parameters, and statistical models to determine how these factors influence the outcome of the data and how these factors influence the interpretation of that data. In this manner, each student will be exposed individually to the decision issues and interpretation pitfalls involved in fMRI data analysis. Cross listed with PSYC 3029.
COMP 4591 Computational Geometry (4 Credits)
This class deals with the design and implementation of efficient algorithms for problems defined over geometric objects, such as points, lines, polygons, surfaces, etc. The methods and algorithms covered find applications in many areas, including computer graphics (e.g., hidden surface removal), computer-aided design and manufacturing (e.g., 3D printing), machine learning (e.g., supervised and unsupervised classification), geographic information systems (e.g. terrain visibility), robotics (e.g., motion planning), data mining (e.g., dimensionality reduction), and computer vision (3D reconstruction), to name a few. Fundamental geometric problems such as partitioning, proximity, intersection, convexity, visibility, point location, and motion planning are focused on. Efficient data structures and algorithms for their solutions and design techniques germane to the field, such as divide-and-conquer, plane sweep, randomization, duality, etc. are discussed in detail. Practical methods for the robust implementation of geometric algorithms are also covered. Prerequisites: COMP 3200 and COMP 3371. This course satisfies the Theory requirement for graduate students.
BUS 4133 Analytics (4 Credits)
Businesses make decisions and improve processes using their own and external data with a variety of data-driven and analytic techniques. This course introduces students to the business data landscape, data management in commercial organizations, and the data-driven decision-making process. Students explore the fundamental concepts behind how data and analytics can improve business performance, using their individual roles and companies as subject matter. Principal Content Elements: 1. Data-driven decision-making and performance improvement. 2. Data management in organizations. 3. Organizational transformation based on data-driven insights.
BUS 6002 Quantitative Methods I- Making Discoveries with Data (4 Credits)
As a PhD student you will do original research … making discoveries that nobody else has made before. Data analysis is a key tool that facilitates that. Data analysis tools help you unlock the hidden treasures within your data set. These treasures are knowledge and information that is waiting to be discovered and utilized for your benefit. Specifically, you will become familiar with several of the internationally utilized statistical software packages and with the array of statistical analysis techniques. You will understand which statistical analysis technique to use in which situation, and how to interpret the output from your statistical software packages. These skills support managers for better decision making. Managers in business and industry have the resources to accumulate data, and this course develops the techniques to discover the information that your data provides. You will also gain skills in understanding how data collection and analysis will benefit your research.
XMBA 4364 Business Data & Analytics (2 Credits)
This course will familiarize the student with data management and analytic methodologies that are prevalent across most industries today, and will suggest a way-ahead as electrons continue to get cheaper to collect and maintain. A well-designed architecture for collecting, storing, and accessing data is essential for all businesses that want to compete successfully as the pace of the decision-making cycle continues to increase. Traditional statistical techniques are still prevalent (and useful!) with proper mining or sampling of big data, and these remain the workhorses of Business Analytics. Analytic modeling is an integral part of business decision-making, and knowing and identifying the appropriate technique can make the difference between discovering the truth and running into a data wall. With the right toolset, the data analyst can tackle large volumes of data with a “divide and conquer” approach. However, the decisions that lead to parsing the data appropriately require not only an understanding of the data and the available tools, but the question being answered as well.
INTS 4057 Statistical Methods I and II (4 Credits)
This is a fast-paced course which serves as an introduction to basic and intermediate concepts in statistics and probability, as well as the primary methods of statistical inference. Topics include data collection, presenting data in tables and charts, summarizing and describing numerical data, basic probability, discrete probability distributions, normal distribution, sampling distributions, confidence interval estimation, single-sample and two-sample hypothesis testing, analysis of variance, chi-square goodness-of-fit tests, chi-square contingency analysis, simple regression and multiple regression. Emphasis will be on statistical reasoning, problem solving, computer applications (using Stata), and interpretation of results. Prerequisite: Strong quantitative background and a minimum quantitative GRE score of 148 or permission of the instructor.
LIS 4230 Database Management Systems (3 Credits)
This is a foundation course on the principles of database design and the use of database management systems for information professionals. The course covers database systems, data modeling, relational models, relational algebra, SQL, emerging NoSQL systems, data storage and querying, query languages, query optimization, OLAP, transaction management, data warehousing, and data mining. In addition, fundamentals on systems analysis and the database application lifecycle will be reviewed.
LIS 4235 Scripting for Large Databases (4 Credits)
This course will introduce students to the basics of data storage and acquisition as part of a multi-step data gathering, processing, analysis and visualization effort. The logic and structure of relational databases will be reviewed, exploring the more common databases like SQL Server and Postgres. along with exploration of JSON and NoSQL based data stores. Techniques and methods for automation and scalable data processing will be introduced under the Pythonprogramming language with a focus on using Pandas and other libraries to simplify data tasks. These skills will be integrated and applied by the student through the use of prepared data sources, along with use of APIs and web scraping technique to acquire data through internet sources.
PSYC 4360 Programming Psychology: Experiment Building with Python (4 Credits)
This graduate-level course provides an introduction to computerprogramming. The goal of the course is to help psychology students develop practical coding skills in Python that will allow them to design and create complex, computer-based experiments. Students will also learn to analyze and plot data. No previous experience with programming is required (or expected). The course begins with an introduction to basic principles of programming with Python. From there, students learn to code by solving challenges specific to the design/construction of a psychological/vision-based experiment. The class is highly interactive— each class includes a mixture of lecture, group-based problem solving, and coding in teams or individually. This class is highly recommended for students who wish to improve their programming proficiency before enrolling in PSYC 4365, although it is not a prerequisite.
COMP 4441 Introduction to Probability and Statistics for Data Science (4 Credits)
The course introduces fundamentals of probability for data science. Students survey data visualization methods and summary statistics, develop models for data, and apply statistical techniques to assess the validity of the models. The techniques will include parametric and nonparametric methods for parameter estimation and hypothesis testing for a single sample mean and two sample means, for proportions, and for simple linear regression. Students will acquire sound theoretical footing for the methods where practical, and will apply them to real-world data, primarily using R. Prerequisites: COMP 1671, MATH 1951, MATH 1952; or Data Science Bridge Courses COMP 3005, 3007, and 3008.
INFO 4240 Data Warehousing (4 Credits)
This course introduces students to the main components of a data warehouse for business intelligence applications. Students will learn how a data warehouse fits into the overall strategy of a complex enterprise, how to develop data models useful for business intelligence, and how to combine data from disparate sources into a single database that comprises the core of a data warehouse. Students will also explore how to define and specify useful management reports from warehouse data.
Prerequisites: INFO 4100, INFO 4140.
INFO 4260 Data Management Platforms (4 Credits)
This course introduces students to the variety of data management platforms being used across the business landscape, and develops skills in using those platforms to manage data and perform analytics. These include Hadoop distributed file systems, Amazon Web Services, Microsoft Azure, and/or other locally-hosted and cloud-based services. Other topics, such as Apache Spark and High-Performance Computing may be introduced using University and College resources.
Learning Outcomes:
1. Students will create portals to data management systems and will run instances of these systems from their local environments.
2. Students will create file systems and load data onto local and cloud-based systems, and will query and manage data within these systems.
3. Students will leverage existing software packages (system-sourced and user-installed) in order to perform analytic modeling on the data in these environments.
4. Students will articulate the advantages and disadvantages of the various data management systems, and assess their utility for a variety of business applications.
INFO 4360 Complex Data Analytics (4 Credits)
This course addresses the rapidly-growing demands on businesses created by the prevalence of big and unstructured data. These include management of big data, big-dataanalytics, analysis of unstructured data (to include text mining), and management and analysis of real-time (streaming) data. The focus will be on enhancing business decision-making in the presence of big data, and on how to create the greatest ROI with large data sets.
INFO 4830 Executive Education – Data Analytics for Decision Making (2 Credits)
In this Executive Education workshop, students will explore how companies organize around data and analytics and how leaders use data to make decisions. Every organization has data, but not every organization knows how to leverage it. This course focuses on the process of analyzing data so that you can discover what problems data can solve and what successes data can make possible. The course will also provide a focus on analytic modeling, using regression analysis and optimization to develop familiarity and skills in the analytic process, and will culminate with an opportunity to explore the challenges that participants’ organizations are facing around their data-driven decision processes. *This short-form workshop does not follow the traditional quarter schedule. Please check daniels.du.edu/executive-education for class dates and formats.
COMP 4581 Algorithms for Data Science (4 Credits)
This course introduces the design and analysis of algorithms within the context of data science. Topics include; asymptotic complexity and algorithm design techniques such as incremental, divide and conquer, dynamic programming, randomization, greedy algorithms, and advanced sorting techniques. Examples to illustrate techniques are drawn from multi-dimensional clustering (k-means and probabilistic), regression, decision trees, order statistics, data mining using apriori algorithms, and algorithms for generating combinatorial objects. Prerequisites: COMP 3006 and 3008.
INFO 4855 Executive Education – Data Visualization Using Power BI (2 Credits)
A picture is worth a thousand words—or a thousand spreadsheets. In today’s complex business world, where the amount of data is overwhelming, being able to create and communicate through compelling data visualizations is a must-have skill for all business professionals. For too long data has been trapped behind scripts, wizards and code. That can change!
This Executive Education workshop is a deep dive into the world of data and data visualization. You will learn how to create, analyze and evaluate large data sets that will enable you to turn mountains of raw data into meaningful stories that inform decisions and drive change. This is a software-heavy class where you will have the opportunity to practice technical skills in Microsoft Power BI, a free software application that lets users visualize data.
*This short-form workshop does not follow the traditional quarter schedule. Please check daniels.du.edu/executive-education for class dates and formats.
FIN 4830 Econometrics for Finance (4 Credits)
Econometrics for Finance is designed to teach applied statistical tools relevant to understanding financial and economic data. It is designed to cover essential tools for working with financial data, including return forecasting, volatility and econometrics of asset pricing, such as testing market models. The emphasis is on empirical techniques which are used in the analysis of financial markets and how they are applied to actual data. It teaches how to use and apply techniques using R, a free software that is used by many finance professionals. The course is intended to prepare students to possess the quantitative tools to evaluate and implement in the finance arena. Prerequisite: STAT 4610 and FIN 4500 (If no experience in R).
STAT 4610 Business Statistics (4 Credits)
This course introduces students to basic analytical tools in statistics and operations management, and provides theoretical concepts and skills that are building blocks for future courses. The approach is to present students with a "corporate" view of how statistical tools are used to analyze data and facilitate business decision-making. Students will familiarize themselves with all of the statistical techniques and models presented in the course and will demonstrate knowledge in applying the appropriate techniques and models to various data sets and interpreting the results of the analysis. The Microsoft Excel Data Analysis and Solver Toolkits will be used to conduct statistical analyses, allowing students to become more proficient overall in using Microsoft Excel and to place their emphasis on applications to core business disciplines, statistical reasoning, and proper interpretation of results. A rich variety of such problems and settings will be discussed in class.
PPOL 4300 Quantitative Analysis-Pub Pol (4 Credits)
This course will provide the MMP student with the tools of mathematical analysis needed for the advanced study of public policy issues and evaluation of alternatives. Topics will include descriptive statistics, probability, sampling, estimation, inference and hypothesis testing, variable analysis and correlation, regression theory, reliability and validity, and prediction and simulation. Students needing review of college-level algebra will be referred to appropriate tutorials. The overall learning objective of this course is to help students recognize and apply basic statistical concepts to Public Policy and, more in general, Social Science analysis. Students will learn how to use statistical software to: build datasets, describe data in a visual and analytical fashion, perform statistical tests, and construct basic statistical inference models. Students will also learn how to report their analytical findings for Public Policy analysis.
LIS 4220 Data Curation (3 Credits)
Across the academic domains, digital data are becoming more visible as critical products of scholarly work. Digital technologies, such as sensor networks in the environmental sciences, social networking tools in the social sciences, and the digitization of cultural artifacts in the humanities, allow researchers to produce far greater volumes and complexities of digital data than were possible in the past. Digital technologies, and the data that they produce, offer tremendous opportunity for researchers in every academic discipline to ask questions that were previously impossible to study. Some digital technologies enable researchers to study very local phenomena in great detail. Others enable the integration of many diverse data streams in order to conduct synthesis and longitudinal studies. But while the possibilities of digital data are exciting, they also present tremendous challenges: how to best organize and manage data, how to make data discoverable and accessible to diverse user communities, and how to store and preserve data over the long term.
COMP 3004 Foundations in Discrete Structures & Algorithms (4 Credits)
Both discrete mathematics and an understanding of algorithms along with their analysis form principle foundations in computer science. In this course, the fundamentals of discrete mathematics including functions, relations, counting, logic, proofs, counting, recurrences, and probability are covered. In addition, beginning data structures and algorithms are covered including linked-lists, graphs, hash-tables, sorting, and binary search. An analysis of these data structures and algorithms is also covered through big-O notation and proof methods.
COMP 3421 Database Organization & Management I (4 Credits)
An introductory class in database management systems covering both relational and non-relational databases with an emphasis on relational. Topics include database design, ER modeling, relational algebra, SQL, scripting, and embedded SQL. Each student will design, load, query and update a nontrivial database using a relational database management system (RDBMS). In addition, an introduction to a NoSQL database will be included. Graduate students will read one or two relevant technical papers and write a summary report. Prerequisites: for undergraduates: COMP 1353 or COMP 2673; for graduates: COMP 3005.
INFO 4300 Predictive Analytics (4 Credits)
This course is designed to prepare students for managerial data analysis and data mining, predictive modeling, model assessment and implementation using large data sets. The course addresses the how, when, why and where of data mining. The emphasis is on understanding the application of a wide range of modern techniques to specific decision-making situations, rather than on mastering the theoretical underpinnings of the techniques. The course covers methods that are aimed at prediction, forecasting, classification, clustering and association. Students gain hands-on experience in using computer software to mine business data sets. Prerequisite: STAT 4610.
INFO 4401 Business Analytics Fundamentals (4 Credits)
Business Analytics is a broad term that describes the process of using data to make business decisions. Data driven business decisions are both critical in modern business and hard to produce with reliable outcomes. This course introduces students to decision-making using probability and other statistical techniques to support and validate the chosen decision. Students will practice hands on business analytics skills for making data driven business decisions.
INFO 4875 Executive Education – Strategic Advantage Using Data Analytics (2 Credits)
In today’s business world, labor, capital, raw materials, and data are all essential to an organization’s strategy. Many leaders have well-developed strategies for the first three, but they lack the understanding and direction to tackle the fourth: data.
This Executive Education workshop focuses on how to build and implement a data strategy to improve organizational performance. Data and analytics programs offer great potential value, and to be effective they must align strategically across the business to deliver a positive return on investment. By understanding and integrating the five main components of a data strategy – Program, People, Process, Platform and Data – you will be able to grow your business and accelerate progress toward your organization’s goals.
*This short-form workshop does not follow the traditional quarter schedule. Please check daniels.du.edu/executive-education for class dates and formats.
BUS 6003 Quantitative Methods II - Making Discoveries with Data (4 Credits)
As a PhD student you will do original research … making discoveries that nobody else has made before. Data analysis is a key tool that facilitates that. Data analysis tools help you unlock the hidden treasures within your data set. These treasures are knowledge and information that is waiting to be discovered and utilized for your benefit. These skills support managers for better decision making. Managers in business and industry have the resources to accumulate data, and this course develops the techniques to discover the information that your data provides. In this course you will learn how these data analysis tools are used for research, and you will plan how you will use your data analysis skills to perform your own research for your doctoral degree.
INTS 4060 Data Visualization (4 Credits)
“The simple graph has brought more information to the data analyst’s mind than any other device,” stated John Tukey, a mathematician distinguished for his contributions to the field of statistics. The course, “Data Visualization” will introduce students to the Grammar of Graphics philosophy which has fundamentally changed thinking about data visualization in the last 20 years. We will use two popular data visualization tools designed using this philosophy: Tableau and the ggplot2 package in R. Students will create a portfolio in which their data visualizations implement best practices — and avoid common pitfalls — to effectively deliver insights.
GEOG 3040 GPS for Resource Mapping (4 Credits)
This course is an introduction to GPS (Global Positioning Systems) concepts, techniques, and applications as they relate to GIS data collection. Lectures focus on satellite surveying, GPS technology, error sources, program planning, data collection design, and Quality Control and Quality Assurance issues for data collection programs. Hands-on lab exercises include navigation, mission planning for a GPS survey, designing a field data collection plan and associated data dictionary, field data collection, differential correction, and data integration into a GIS and map production.
GEOG 3120 Environmental/GIS Modeling (4 Credits)
Facing challenges brought by the dramatically changing global environment, environmental modeling is increasingly used to support geographical and environmental decision making (e.g., spatial conservation prioritization). Environmental modeling is concerned with the characterization, modeling and simulation of environmental phenomena and processes using conceptual and mathematical models. Environmental phenomena and processes taking place in the geographic space are regulated by spatial principles. They also interact with other phenomena or processes in the attribute space. For example, species distribution is not only constrained by spatial factors such as proximity to other species, but also influenced by environmental factors such as terrain and climatic conditions. Due to its superior capabilities of handling spatial data and modeling spatial and attribute relationships, geographic information system (GIS) provides the ideal tools for environmental modeling. This upper-level undergraduate/graduate-level course surveys the concepts and techniques of GIS supported environmental modeling in three general categories: 1) Modeling in the spatial domain where the focus is on modeling spatial principles (e.g., spatial autocorrelation); 2) Modeling in the attribute domain where the emphasis is on environmental correlations (e.g., environmental niche modeling); 3) Modeling in the combined spatial and attribute domain where both spatial principles and environmental correlations are exploited (e.g., geographically weighted regression). Throughout this course, several real-world applications are used to demonstrate the ideas, concepts, and techniques of GIS supported environmental modeling, including crime spatial pattern modeling, species distribution modeling, and soil-landscape modeling and mapping. Prerequisites: GEOG 2000 and GEOG 2100.
ANTH 3630 Archaeological Method and Theory (4 Credits)
This class presents methods for gathering archaeological data in the laboratory and then using a variety of theoretical approaches in its interpretation. Students gather archaeological data using museum collections from a variety of sites. Those artifacts include stone tools and ceramics as well as other environmental data and architectural information in a variety of environmental and landscape contexts. For each site studied students are presented with a body of theoretical literature from which to interpret these data. A variety of interpretative methods can potentially be chosen for each site, and in most cases there is no right answer, only answers that can be supported by the data collected and interpreted using the theoretical constructs read. All students are required to write up complete site reports for each project including all raw data collected in the analysis and theoretical approaches used in interpretation.
COMP 3005 Foundations in Python Programming (4 Credits)
This accelerated course covers the basics of Pythonprogramming. By the end of the course students will be able to develop, design and implement Python programs, explain the differences between data types, learn to read from and write to files, understand and use data structures, understand and use recursion, and use Python packages.
COMP 3371 Data Structures & Algorithms (4 Credits)
Design and analysis of algorithms and data structures; asymptotic complexity, recurrence relations, lower bounds; algorithm design techniques such as incremental, divide-and-conquer, dynamic programming, iterative improvement, greedy algorithms; randomized data structures and algorithms. Prerequisites: COMP 2370 or equivalent and COMP 3200.
COMP 4447 Data Science Tools 1 (4 Credits)
Organizations are using data science to extract actionable insight from data. To highlight the hidden patterns in the data, this course equips students with essential sills for data collection, cleanup, transformation, feature engineering, summarization, and visualization. Students will do assignments and a final project. This is a hands-on course. Students will use Python libraries, Linux commands, and various data sets to perform these activities. Enforced Prerequisites: COMP 3006 and COMP 3008. Co-requisite: COMP 4441.
MBA 4160 Statistical Learning (2 Credits)
This course will allow the student to develop an understanding of more complex concepts of probability and statistics and how they relate to managerial type problems and decision making. These will include differentiating different data types and determining their appropriate analyses (descriptive, visual, and statistical including comparing means/proportions and regression.) In addition, the student will experience performing, interpreting, and presenting these probability and statistics methodologies.
PSYC 4355 Multilevel Modeling for the Psychological Sciences: Theory and Applications (4 Credits)
This advanced course covers the basics of multilevel (hierarchical) linear modeling and how this flexible approach to statistical analysis can be applied to theory and data in the psychological sciences. Specific techniques that will be covered include the analysis of nested data, family and dyadic data, and longitudinal data as well as mediation and moderation. There will be an emphasis on applying these techniques to students' own research through hands-on demonstrations and homework assignments. There will also be an emphasis on interpreting and critiquing multilevel modeling analyses in published research. Courses on analysis of variance as well as correlational methods and regression are pre/corequisites.
PSYC 4365 Programming Psychology: Model-Fitting and Analysis (4 Credits)
An introduction to creating, fitting, and performing statistical inference using computational models with an emphasis on binary choice data. The aims of this course include familiarizing students with the mathematical basis of model-fitting, learning the value of taking a variety of approaches to fitting trial-by-trial data, and giving students practical hands-on experience with maximum likelihood fitting methods. This course will use both MATLAB and R. Though not a prerequisite, this course is intended to follow Programming Psychology: Experiment Building in MATLAB (PSYC 4360), and so will assume students already have a basic knowledge of coding in MATLAB (including debugging, scripts, functions, loops, and plotting). This course is open to graduate students outside of the Department of Psychology.
COMP 3372 Advanced Algorithms (4 Credits)
Advanced techniques for the design and analysis of algorithms and data structures; amortized complexity, self-adjusting data structures; randomized , online, and string algorithms; NP-completeness, approximation and exact exponential algorithms; flow networks.
COMP 3431 Data Mining (4 Credits)
Data Mining is the process of extracting useful information implicitly hidden in large databases. Various techniques from statistics and artificial intelligence are used here to discover hidden patterns in massive collections of data. This course is an introduction to these techniques and their underlying mathematical principles. Topics covered include: basic data analysis, frequent pattern mining, clustering, classification, and model assessment. Prerequisites: COMP 2370.
COMP 4370 Algorthmic Problem Solving (4 Credits)
The course is intended for students who are familiar with programming syntax but have not had much experience writing computer programs to solve a problem stated as a high-level description. The course will run through multiple such problem descriptions, discuss the design of programs to solve those problems using popular data structures, and have students implement those designs using a programming language. This course does not count for MS Computer Science requirements. Prerequisites: COMP 3001, 3002, 3003, and 3004.
COMP 4372 Advanced Algorithms (4 Credits)
Advanced techniques for the design and analysis of algorithms and data structures; amortized complexity, self-adjusting data structures; randomized , online, and string algorithms; NP-completeness, approximation and exact exponential algorithms; flow networks. Prerequisite: COMP 3371. Cross listed with COMP 3372.
COMP 4431 Data Mining (4 Credits)
Data Mining is the process of extracting useful information implicitly hidden in large databases. Various techniques from statistics and artificial intelligence are used here to discover hidden patterns in massive collections of data. This course is an introduction to these techniques and their underlying mathematical principles. Topics covered include: basic data analysis, frequent pattern mining, clustering, classification, and model assessment. Prerequisites: COMP 4441 and COMP 4581.
COMP 4442 Advanced Probability and Statistics for Data Science (4 Credits)
This course builds on material in Probability and Statistics 1. Students will carry out model fitting and diagnostics for multiple regression, ANOVA, ANCOVA, and generalized linear models. Dimension reductions techniques such as PCA and Lasso are introduced, as are techniques for handling dependent data. The course introduces the principles of resampling and Bayesian Analysis. Students will acquire sound theoretical footing for the methods where practical, and will apply them to real-world data, primarily using R.
Enforced Prerequisites:
COMP 4441.
COMP 4510 Software for AI Robotics (4 Credits)
This course provides an introduction to the key artificial intelligence issues involved in the development of intelligent robotics. We will examine a variety of algorithms for autonomous mobile robot behavior, exploring issues that include software control architectures, localization, navigation, sensing, planning, and uncertainty. We also introduce the Robot Operating System (ROS) middleware, which is popular in academic, industry, and government research. This course does not assume any prior knowledge of artificial intelligence or robotics. The course will be project focused. In the project assignments you will learn ROS and learn to implement algorithms essential for conducting AI robotics research. Prerequisites: COMP 3005 and proficiency in Python and Unix command-line tools.
ENSY 4024 Applied Electrical, Mechanical and Software Systems (3 Credits)
Advanced optimizationalgorithms are presented, as a pillar of data science and machine learning. Topics include: linear, nonlinear and integer programming models. Students will learn to understand tractability of models, particularly complex models as are central to the discipline of Systems Engineering. Prerequisite: ENSY 4040.
ENSY 4030 Introduction to Aerospace Missions (3 Credits)
Digital engineering technologies address the difficulties of managing complex and evolving technologies over their lifecycles of (i) development and (ii) operations & maintenance. This course will focus on digital technologies to integrate data across the enterprise, break organizational silos, and drive culture to realize risk reduction. Topics include: elements of the digital thread, such as digital twins and simulation, as well as machine learning and dataanalytics to inform decision-making throughout the lifecycle.
Applied mathematics for engineers. Systems and series solutions of ordinary differential equations, Fourier analysis, partial differential equations, linear algebra, vector calculus, special functions, unconstrained and combinatorial optimization, and applied probability and statistics. Prerequisites: MATH 2070 and MATH 2080 or instructor permission.
ENGR 4620 Optimization (4 Credits)
The development and application of various optimization techniques will be explored with engineering examples. Topics include: analytical and numerical methods, linear and non-linear programming techniques for unconstrained and constrained problems, and advanced optimization techniques, e.g. global optimization. Optimization methods will be developed and evaluated in code and used in a real-world application project.
INFO 4100 Survey of Business Analytics (4 Credits)
This course provides an overview of business analytics: how business data are collected, processed, and analyzed to support decision making. It will address both how to assess and use data that is readily available as well as how to start with corporate strategy and determine what data is needed, how to generate and process it. The course will also explore how corporate culture, ethics, and globalization can affect data management and analytic decision-making.
MBA 4265 Introduction to Analytics (2 Credits)
Businesses make decisions and improve processes using their own and external data and a variety of modeling and analytic techniques. This course introduces students to the business data landscape, data management in organizations, the data-driven decision-making process, and the fundamental concepts behind statistical inference and analytic modeling to support decision-making.
INTS 4052 Statistical Methods III (4 Credits)
This course will serve as continuation of Statistical Methods II. This will be an applied, non-calculus based course on statistical techniques used in nonparametric and multivariate analysis. Emphasis will be on applications and data analysis using the statistical software package SAS. Prerequisite: INTS 4051 or INTS 4057.
INTS 4423 Introduction to Epidemiology (4 Credits)
Decisions and policy related to global health are based on data from various disciplines such as demography, medicine, and epidemiology. Therefore, it is crucial to correctly understand and interpret what health data and the data in general tell us. This course provides the knowledge and skills required to critically assess data, and understand both strengths and limitations of data and research. This course covers the basic principles and concepts of descriptive and analytic methods in epidemiology and their application to research and practice in public and global health.
LAWS 4145 Computer and Internet Law (3 Credits)
Computers and Internet Law is designed to consider the areas in which computer technology and the legal environment intersect. This includes legal protection of computer software; contracting for computer services; computerdata banks and privacy; the check-less society; and the relationships between Federal Communications Commission policies and computers.
The use of statistics in all branches of anthropology; data screening; parametric and nonparametric statistics. Prerequisite: any course in basic statistics.
ANTH 3875 Research Methods in Anthropology (4 Credits)
This course offers an in-depth introduction to anthropological research methods with the aim of providing students with the tools necessary to design a coherent research proposal. Starting with the notion that anthropological research is a scientific endeavor, the course offers knowledge and skills that allow for a systematic application of qualitative and quantitative methods to respond to research questions. Students will learn when and how to use one method, as well as the implications of doing it. Students will also learn how to critically read research reports that use qualitative, quantitative, or mixed methods. The course is organized in two portions. The qualitative portion will focus on a detailed exploration of the continuum that goes from posing a research question, choosing a methodology, carrying it on, and reporting the results. The quantitative portion is concentrated on collecting numerical data, methods of which are often based on a qualitative understanding of people. Quantitative analysis will present tools used to take readings, acquire data, observations, and other information necessary to test hypotheses about people, cultures and how we can understand them from their material remains. The purpose of the quantitative part of the class is to determine what is statistically significant and what ideas about people are supportable using the scientific method. This course is required for all anthropology graduate students, and suggested for advanced undergraduates who are working on senior theses, and have an interest in anthropological research. The course is also open to non-anthropology students interested in anthropological research.
COMP 3007 Foundations in Data Science Mathematics I (4 Credits)
This course presents the elements of calculus essential for work in data science. Students will study differentiation and integration in the context of probability density and of optimization.
COMP 3200 Discrete Structures (4 Credits)
Discrete mathematical structures and non-numerical algorithms; graph theory, elements of probability, propositional calculus, Boolean algebras; emphasis on applications to computer science. Cross-listed as MATH 3200. Prerequisites: (COMP 2300 or MATH 2200) and (COMP 2673 or COMP 1353).
This course introduces unmanaged programming language concepts to students whose primary programming experience is in a managed language (Java/Python, etc.). Concepts like type safety, manual memory management and “unsafe” library functions are covered. Common pitfalls in these languages from which most security issues arise are explained and students gain experience in understanding such code and evaluating it for program errors. Students will also be introduced to important compiled language concepts of static/dynamic linking, compilation and debugging. Prerequisites: COMP 3006.
COMP 3501 Introduction to Artificial Intelligence (4 Credits)
Introduces a variety of Artificial Intelligence concepts and techniques, relevant to a broad range of applications. Students survey multiple techniques including search, knowledge representation and reasoning, probabilistic inference, machine learning, and natural language processing. Examines concepts of constraint programming, evolutionary computation and non-standard computation. Prerequisites: COMP 2673 or COMP 1353.
COMP 4100 Human-Computer Interaction (4 Credits)
Introduces students in computer science and other disciplines to principles of and research methods in human-computer interaction (HCI). HCI is an interdisciplinary area concerned with the study of interaction between humans and interactive computing systems. Research in HCI looks at cognitive and social phenomena surrounding human use of computers with the goal of understanding their impact and creating guidelines for the design and evaluation of software, interfaces, physical products, and services in industry. No prerequisites are required to take the course and students from all disciplines are welcome. Cross listed with COMP 3100.
COMP 4531 Deep Learning: Model Design and Application (4 Credits)
This course addresses the foundational concepts and components of Artificial Neural Networks (ANN), highlighting their capabilities, strengths, and weaknesses as a machine learning algorithm. Students taking this course will develop ANN models from scratch in Python as a basis for understanding their design as well as the underlying mechanics and calculations that shape their behavior. Key topics such as forward-backward propagation, loss function characteristics and optimization will be considered in relation to model design and computational efficiency as well as to problems such as exploding and vanishing gradients. Training strategies (e.g., dropout, initialization, batch normalization) will further enable students to assess trade-offs in model bias & variance. Coupled with hands-on assignments, these building blocks provide the knowledge and skills required to effectively design and implement ANN models that are ethically and technically sound. As well as foreground important architectures such as Convolutional ANNs, Recurrent ANNs, LSTMS, and Transformers as well as their applicability to modern problems. Student learning and proficiency will be assessed based on a combination of quizzes, coding assignments, exams, and a culminating project. Prerequisite: COMP 4432.
ENEE 4950 Graduate Assessment (0 Credits)
Introduction to network components. Layering of network architecture. Analysis of Local Area Network (LAN) concepts and architecture based on IEEE standards. Design principles including switching and multiplexing techniques, physical link, signal propagation, synchronization, framing and error control. Application of probability and statistics in error detecting and control. Ethernet, Token-ring, FDDI (Fiber Distributed Data Interface), ATM (Asynchronous Transfer Mode), ISDN (Integrated Service Data Networks). Prerequisite: ENEE 3111, ENCE 2101 or permission of instructor.
ENCE 3231 Embedded Systems Programming (4 Credits)
Design of Very Large Scale Integration systems. Examination of layout and simulation of digital VLSI circuits using a comprehensive set of CAD tools in a laboratory setting. Studies of layouts of CMOS combinational and sequential circuits using automatic layout generators. Fundamental structures of the layout of registers, adders, decoders, ROM, PLA's, counters, RAM and ALU. Application of statistics and probability to chip performance. CAD tools allow logic verification and timing simulation of the circuits designed. Cross listed with ENCE 4501. Prerequisite: ENCE 3231.
ENCE 5995 Independent Research (1-18 Credits)
Introductory course on modern digital communication systems. The basic communication system theory, probability and random processes, baseband digital data transmission, coherent and non-coherent digital modulation techniques and analysis of bit error probability. Bandwidth efficiency and transmission of digital data through band-limited channels. Prerequisites: ENEE 3111, ENGR 3611 or permission of instructor.
ENEE 3641 Introduction to Electromagnetic Compatibility (4 Credits)
Introductory course on modern digital communication systems. The basic communication system theory, probability and random processes, baseband digital data transmission, coherent and non-coherent digital modulation techniques and analysis of bit error probability. Bandwidth efficiency and transmission of digital data through band-limited channels.
ENSY 4022 Design of Space Systems Part 2 (3 Credits)
The development and application of various optimization techniques will be explored with engineering examples. Topics include: analytical and numerical methods, linear and non-linear programming techniques for unconstrained and constrained problems, and advanced optimization techniques, e.g. global optimization. Assignments are in context of Systems Engineering case studies.
INFO 4390 Advanced Predictive Modeling with R (4 Credits)
This course serves as an introduction to advanced predictive modeling and statistical learning using the Rstatistical software. Specific topics include linear, non-linear, and logistic regression, classification, resampling methods, and non-linear regression, tree-based methods, and support vector machines. The students will learn how to communicate their results (business reports, dashboards, etc.) of the varioius modeling exercises and projects using RStudio and the RMarkdown suite of tools. Enforced Prerequisites and Restrictions: INFO 4100 and INFO 4300.
INFO 4610 Business Statistics and Analytics (4 Credits)
Making high quality business decisions is hard. Using data to make business decisions makes the process better. This course introduces students to a variety of techniques in analytics and statistics that facilitate data driven business decisions. Time will be spend identifying appropriate techniques to apply in various scenarios, applying in detail some of the quantitative techniques, and using analytic outputs to inform business decisions. Both technical skills and clear communication of results and decisions will be covered. Choosing proper techniques, technical work using Microsoft Excel, proper interpretation of results, and decision making are skills practiced in this course.
BUS 6000 Research Methods in Business (4 Credits)
Business Research Methods introduces students to the nature, scope, and significance of research and research methodologies. Additionally, the course studies primary and secondary research methods with applications to specific problems, using qualitative and quantitative designs for individual investigation on current problems within a student's area of interest. Topics covered include research design, sampling strategy, data types and collections, measurement approach, testing procedures, ethics in data collection and interpreting findngs, and the Institutional Review Board (IRB) process.
MGMT 4306 Virtual Business Management Simulation (2 Credits)
The focus of this course is on gaining new venture experience. Through an online/virtual computersimulation, students will be placed into a very realistic international business setting, where they will start up and run a company through multiple rounds of decision-making. The online simulation allows students to build entrepreneurial firms, experiment with strategies, and compete with other student teams in a virtual business world. Designed to mimic the competitive, ever changing marketplace, the simulation lets students gain experience in market analysis, strategy formulation, and the management of a new venture.
MKTG 4860 Data Science for Marketers (4 Credits)
Data is an essential part of (digital) marketing. In fact, data enables the promise of digital marketing: real-time feedback enabling businesses, marketing campaigns to pivot and become predictive. We’ll cover what it takes to become a data-driven organization and how to tell stories through data.
ACTG 4155 Accounting Data Skills and Concepts (4 Credits)
This course is designed to give students an understanding of the technology underlying accounting information systems and help students develop more advanced data analysis skills. We will use the programming language Python to develop an understanding of the digital business logic that supports the operations of modern firms. We will learn to use Business Process Modeling Notation (BPMN) to graphically document operations and their underlying business logic. We will discuss and analyze a set of studies that use survey data from a global sample of executives and analysts to develop an understanding of the levels of technological sophistication in modern firms. We will also discuss and analyze distributed databases, information security, and eXtensible Business Reporting Language. Prerequisites: none.
EVM 4404 Primary Research (1 Credit)
To be successful in your business venture, you need to make data-driven decisions. Much of that data can come from internal operations or perhaps secondary sources. But, to truly be successful, you need to gather, analyze, and make decisions based on primary research.
In this course, you’ll learn the basic tenets of performing primary research activities including defining your business problem, developing research questions, identifying your market segment, building a primary research instrument(s), gathering data using a primary research instrument, analyzing the data, and making recommendations.
STAT 4100 Quantitative Methods I (4 Credits)
An introduction to the methods of quantitative analysis commonly used in business, with an emphasis on finance applications. Topics include descriptive statistics, probability, probability distributions, fundamentals of statistical inference, correlation, and simple and multiple regression analysis.
STAT 4500 Prob Thry Math Gamb (4 Credits)
This course covers the theory of probability and the formal study of mathematics underlying gambling and games of chance. Topics include probability concepts, probability rules, expectation, permutations and combinations, the law of large numbers, the law of "averages," history of gambling, house advantage, fallacies and betting systems, volatility and operations, game odds and price setting, games of pure chance, games with a skill component. Prerequisite: a previous course in statistics or permission of instructor. Cross listed with STAT 3500.
INTS 4050 Statistical Methods I (4 Credits)
An introductory course featuring statistical reasoning, probability, sampling, statistical inference, nominal and ordinal measures of association, and correlation. Open only to students with no prior background in statistics.
INTS 4303 Econometrics for Decision Making I (4 Credits)
The first course in a two course sequence in Applied Econometrics. Introduces basic probabilistic techniques for the quantitative analysis of economic and social data and their application to international public policy decision making. Prepares students to: compile and analyze data sets; build and test regression models; interpret and critically evaluate applied econometric studies; and conduct their own applied econometric research using computerized statistical packages. Prerequisite: INTS 4051 or INTS 4057.
ADMN 4900 Advanced Inquiry and Analysis (4 Credits)
This course is part two of a two-part course series. In part one of this series, Introductory Qualitative Research (RMS 4941), you learned about the foundations of qualitative research including philosophical perspectives, theoretical underpinnings, key characteristics, and common approaches to inquiry and research design: case studies, ethnography, narrative (testimonios), grounded theory, phenomenology, and action research. You ended the course with a design of a qualitative study proposal informed by the extant literature and your personal, practical, and intellectual goals. You completed the course with the design of a qualitative research study. ADMN [xxxx], Advanced Inquiry and Analysis, is the counterpart where you will go in the field to execute your qualitative study designed in your Introductory Qualitative Course. This intermediate level qualitative course builds on the content of other qualitative research courses at the University of Denver. In this course, you will continue to learn the skills and competencies needed to gather, analyze, and report high quality data. You will leave the course well-grounded in the application of the IRB process, data collection, data analysis, data interpretation, handling concerns about reliability, validity, and ethics; and writing the final report. The final product for this course will be the execution of a rigorous qualitative research design with preliminary findings that could be presented at a professional conference and with further development for manuscript publication.
HED 4281 Inclusive Excellence Programming and Development (4 Credits)
IE in Programming and Development will provide an overview related to the development and implementation of cultural programming and cultural centers over time. This course will pay specific attention to the role of student activism in creating change on college campuses in the form of cultural programming, centers, diversity curriculum, and inclusive excellence initiatives. The course will also address the challenges and competencies associated with inclusive excellent programming and development.
LIS 4050 Library and Information Technologies (3 Credits)
A foundation course on the applications of information and communications technology in libraries and information agencies. Integrated library systems and the acquisition, evaluation, and implementation of library automation solutions, including electronic resource management systems are explored. The course further introduces database design, Internet technology, web services, cloud computing, computer networks, telecommunications, and computer security. Hardware, software, and other productivity tools and utilities from organizations such as OCLC, Amazon, and Google are discussed.
LIS 4370 Database Searching (2 Credits)
Nearly all historic, traditional search and retrieval tools such as library catalogs, indexes, microform guides, and archival findings aids have migrated to web-based systems. This course explores the complexities of searching for materials in an online environment. Topics to be covered include database and field structures; controlled vocabularies and indexing schema; search syntaxes, reference linking; data exploring and manipulation; non-textual database searching including numerical, image, and multimedia data; metasearch and web-scale discovery technologies.
RMS 4916 Latent Growth Curve Modeling (4 Credits)
This course covers advanced issues in longitudinal data analysis using structural equation modeling and hierarchical linear modeling with latent variables. It involves both conceptual development and practical implementation of longitudinal data analysis. This course is intended to be a hands-on approach to working with data and addressing research questions that can be best answered by longitudinal data. Prerequisite: RMS 4914.
BIOL 4085 Accelerated Biostatistics (2 Credits)
This is an accelerated online statistics course for graduate students in Biology. Basic probability and hypothesis testing is the foundation of teaching applied statistics, including simple statistics (t-tests, F-tests, and chi square) and more advanced procedures (regression, correlation, analysis of variance). In addition, students learn more complex tools (multiple regression, multi-classification ANOVA, Student-Newman-Keuls tests), including non-parametric Tests (Mann-Whitney U, Sign test, Wilcoxon Rank Sum).
GEOG 3010 Geographic Information Analysis (4 Credits)
Reviews many basic statistical methods and applies them to various spatial datasets. In addition, several spatial statistical methods are applied to spatial datasets. This course is an in-depth study of the interface between GIS, spatial data, and statistical analysis. Preferred prerequisite: GEOG 2000. Prerequisite: GEOG 2100.
GEOG 3100 Geospatial Data (4 Credits)
This graduate-level course is designed to provide graduate students from a broad range of disciplines with the skills to carry out applied research tasks and projects requiring the integration of geographic information system technologies and geospatial data. Students are introduced to a collection of techniques and data sources with a focus on acquiring and integrating data. Legal, ethical, and institutional problems related to data acquisition for geospatial information systems are also discussed.
GEOG 4110 Geospatial Data (4 Credits)
This graduate-level course is designed to provide graduate students from a broad range of disciplines with the skills to carry out applied research tasks and projects requiring the integration of geographic information system technologies and geospatial data. Students are introduced to a collection of techniques and data sources with a focus on acquiring and integrating data. Legal, ethical, and institutional problems related to data acquisition for geospatial information systems are also discussed.
EDPX 4330 Advanced Coding (4 Credits)
This course is focused on text-based creative coding for multiple purposes. Specific applications change each quarter and can include mobile apps, computer vision, machine learning, generative art, programming reactive spaces, web animation, and other emerging ideas, all driven by creative coding. Prerequisite: EDPX 4010.
PHIL 3612 AI and Robotics (4 Credits)
In this interdisciplinary seminar we will discuss foundational issues regarding artificially intelligent systems. We will seek to understand how recent advances in AI research bear on our understanding of the nature of the mind, intelligence, agency, rationality, and consciousness. We will also discuss how philosophical advances can advance empirical progress. Additionally, we will discuss some barriers to progress that these technologies might pose. In particular, we will be focused on three groups of questions: 1. What special opportunities and challenges are presented by deep neural net and deep learning technology regarding building and understanding artificially minded intelligent agents? 2. What is the role of the body and environment in producing intelligence? 3. Deep neural net algorithms are already commonly used to predict recidivism rates, diagnose illnesses, and make advertising more effective. In what ways might such algorithms be approaching human or animal intelligence, or shed light on such intelligence? In what ways might human and animal intelligence be importantly different? In what ways might contemporary intelligence research perpetuate injustice and oppression? This seminar is designed to be interdisciplinary, and I welcome students working in philosophy, robotics/AI, and cognitive science who want to work hard and dig deeper. There are no strict prerequisites, but some background knowledge in relevant disciplines will be highly useful.
COMP 3002 C and C++ Foundations II for New Graduate Students (4 Credits)
This accelerated course continues to build on the basics of discrete mathematics by covering material including advanced counting, recurrences, graphs, trees, traversals, automata etc. that is necessary to attend Computer Science graduate school. In addition, it includes an introduction to additional algorithms and data structures. Prerequisite: COMP 3001.
COMP 3351 Programming Languages (4 Credits)
Learn the fundamentals of programming languages through functional programming through an in-depth understanding of syntax and semantics around program structures and how programming languages are parsed and interpreted. Understand recursion as a fundamental problem-solving paradigm and the important role that higher order types and kinds play in eliminating errors and simplifying software development. Prerequisites: COMP 2370 and ((COMP 2355, COMP 2691) or COMP 2362).
COMP 3821 Game Programming I (4 Credits)
Introduces the fundamentals of digital game programming that are essential as future game programmers or game designers. Students have the opportunity to learn game engine architecture, 2D and 3D linear algebra for graphics, sprites and animations, input handling, finite state machines, particle systems, user interfaces, game audio, and artificial intelligence for games. Prerequisites: COMP 2370 and COMP 2821.
COMP 4333 Parallel and Distributed Computing (4 Credits)
Current techniques for effective use of parallel processing and large scale distributed systems. Programming assignments will give students experience in the use of these techniques. Specific topics will vary from year to year to incorporate recent developments. This course qualifies for the Computer Science "Advanced Programming" requirement. Prerequisites: COMP2370 and COMP2355, or equivalent.
COMP 4334 Parallel and Distributed Computing for Data Science (4 Credits)
Current techniques for effective use of parallel processing and large-scale distributed systems for data science. Programming assignments will give students experience in the use of these techniques. Specific topics will vary from year to year to incorporate recent developments. This course is not to be used for the MS Computer Science. Prerequisite: COMP 4581.
COMP 4448 Data Science Tools 2 (4 Credits)
Building a successful predictive model is a multi-faceted process. This course focuses on hypothesis testing and the development of predictive models. Students will also learn how to perform graph-based modeling and optimization. Students will do assignments and a final project. This is a hands-on course. Students will use Python libraries, Linux commands, and various data sets to perform these activities. Prerequisite: COMP 4447.
ENCE 3501 VLSI Design (3 Credits)
This class covers topics in machine learning including but not limited to Bayesian decision theory, supervised learning, unsupervised learning and clustering, linear discriminant functions, deep learning, neural networks, linear classification techniques, manifold learning, bag of words, and Support Vector Machines. Cross listed with ENCE-4631.
ENCE 3630 Pattern Recognition (4 Credits)
Introduction to microprocessors and to the design and operation of computer systems. A study of the microprocessor and its basic support components. Analysis of CPU architectures of modern computers. Assembly language programming. Use of an assembler and other development tools for programming and developing microprocessor-based systems. Cross listed with ENCE 3210.
ENCE 4501 Advanced VLSI Design (4 Credits)
This class covers advanced topics in machine learning including but not limited to Bayesian decision theory, supervised learning, unsupervised learning and clustering, linear discriminant functions, deep neural networks, deep learning, linear classification techniques, manifold learning, bag of words, and Support Vector Machines. Cross listed with ENCE 3631.
ENGR 4350 Reliability (4 Credits)
An overview of reliability-based design. Topics include: fundamentals of statistics, probability distributions, determining distribution parameters, design for six sigma, Monte Carlo simulation, first and second order reliability methods (FORM, SORM). Most Probable Point (MPP) reliability methods, sensitivity factors, probabilistic design. Cross listed with ENGR 3350.
ENGR 4680 Fault Diagnosis & Prognostics for System Design (4 Credits)
Reliability engineering is a sub-discipline of systems engineering that emphasizes dependability in the lifecycle management of a product. Reliability, describes the ability of a system or component to function under stated conditions for a specified period of time. Reliability is closely related to availability, which is typically described as the ability of a component or system to function at a specified moment or interval of time. Normally, quality focuses on the prevention of defects during the warranty phase whereas reliability looks at preventing failures during the useful lifetime of the product or system from commissioning to decommissioning. Diagnosis is used, with variations in the use of logic, analytics, and experience, to determine "cause and effect". In systems engineering, it is typically used to determine the causes of symptoms, mitigations, and solutions. Prognostics is an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function. This lack of performance is most often a failure beyond which the system can no longer be used to meet desired performance. The predicted time then becomes the remaining useful life (RUL), which is an important concept in decision making for contingency mitigation. Success in this course requires knowledge of probability theory and statistics, and familiarity with MATLAB/Simulink.
ENGR 4755 Optimal Control (4 Credits)
Introduction to optimal control theory (control laws that maximize a specified measure of a dynamical system's performance). Topics include: optimality conditions and constraints; calculus of variations; review of mathematical programming (Language multipliers, convexity, Kuhn-Tucker theorem); Pontryagin's maximum principle (constraints, Hamilitonians, bang-bang control); dynamic programming and Linear Quadratic Regulation (Riccati, Hamilton-Jacobi equation). Prerequisites: ENGR 3721 (Controls) and ENGR 3735/4735 (Linear Systems) or equivalent courses.
ENGR 4940 Mission Operation Controls (4 Credits)
Space operations is based at a centralized control center, a facility used for command & control (C2), and related communication equipment (antennas, etc.). The human operators conduct the day-to-day operations for controlling the spacecraft. They control the spacecraft and its payloads, and carries out all activities related to mission planning and scheduling. For example, normal orbital operations are interrupted every six months to conduct orbital maneuvers. Launch operations begin with spacecraft integration and checked-out for launch. Once safely placed in orbit, command and control goes back and forth between the ground control station and the spacecraft or satellite. A key aspect of spacecraft operations is the transferring of data from the onboard instruments collected by its payload to the ground, eventually disseminating the data to concerned users and analysts through a ground data network. This requires an on-orbit communication architecture.
BUS 4141 Cybersecurity (1 Credit)
You are under attack! Cyberattacks are on the rise and they can be catastrophic to a business resulting in downtime, lost profits, and growing distrust from stakeholders. While there isn’t a perfect solution to stopping cyberattacks, this course is designed to help leaders become better equipped to mitigate these threats by improving their understanding of the current state of cybersecurity, how it’s being used by businesses, and what they can do to better protect themselves from cyberattacks. In addition to learning the application and outcomes of cybersecurity, learners will be exposed to the growing ethical debates surrounding cybersecurity in efforts to be better prepared to make security recommendations for their organizations.
BUS 6005 Behavioral Research Design and Execution (4 Credits)
The first purpose of this course is to prepare doctoral students with the ability to design, implement, and test the results from an experiment or survey. This includes operationalizing independent and dependent variables, rooted in definitions of terms and theory, in a manner that allows for quality statistical testing. Students will also learn manipulation of independent variables, including manipulation checks, and the basics of survey design. Lastly, analysis of existing data sets will round out the learning. This course builds on the previous research and statistics coursework in the program with a focus on practice and application. Multiple actual studies will be replicated as part of the course with the final project being a replication of a study of the student’s choosing.
BUS 6502 Applied Research Practicum Series: III (4 Credits)
Students will design an appropriate scientific method (e.g., survey, experiment or interview) including a data collection and analysis plan per the final proposal submitted in ARP II. Once appropriately designed, under the direction of their ARP professor, students will collect data appropriate to test the study’s hypotheses. Institutional Review Board (IRB) approval must be received prior to data collection which should be of publishable quality (broadly defined).
FIN 4500 Financial Modeling (4 Credits)
Use of various financial software applications to construct models from corporate finance, investments, and financial markets. In particular, the course will cover the application of Excel spreadsheet functions and Rprogramming to various topics including the time value of money, investment projects analysis, financial statements analysis, capital budgeting, portfolio analysis, and data & pivot tables. Prerequisites: FIN 4630.
ITEC 4280 Intro Software Engineering II (4 Credits)
A continuation of ITEC 4270, this course covers systems development in a client-server Internet/Intranet environment using the Javaprogramming language. Principles of event- driven systems, remote database access, and building GUI (Graphical User Interface) prototypes for interfacing with desktop systems are included. Prerequisite: ITEC 4270 or instructor's permission.
ITEC 4478 XML (4 Credits)
This programming course is the second of a five series Web Services course track designed to prepare the student for the certification exam offered by Microsoft in the development of .NET applications. The second module of the series, XML, provides a thorough understanding of the main techniques surrounding the development of XML applications. Up until now, it has been very difficult to communicate and transfer data between different platforms. The surge of XML as a universal text-based standard readable and interpreted by any other system available, has opened the channel to enhance the development of cross-functional applications. Students will learn to write the codes describing the data, processes it and prepare it for presentation, as well as modeling and designing functional components that will later be used to drive the applications. Topics include: creating well-formed and valid XML documents, parsing the documents and creating the format to display it through the client's browser, design functional components and the interconnections among them. Some of the tools that the student will learn to use in this course are XML Syntax, DTD, Schema, CSS, XSL, XSLT, DOM, SAX, SOAP, WSDL, and UDDI. Prerequisite: ITEC 4477 or concurrent enrollment.
MBA 4340 Sustainable Enterprises (2 Credits)
This course provides an overview of sustainability and how it has become relevant to all corporations and their stakeholders. Sustainability is more than financial or environmental – it is a larger concept that includes the economic, social, and environmental aspects of an organization. While there has been debate about corporate responsibility to not cause social or environmental harm, sustainability has become an economic driver of financial performance with specific business risks and opportunities. Why? First, we can better measure the real costs of climate change and other negative externalities and their costs to society, communities, and corporations. Second, investors and other stakeholders are asking companies how prepared they are to manage the business risks and opportunities in their industries. Companies are being asked for data on their environmental impacts and their workforce development and inclusiveness because these affect their future business performance as business risks, revenue opportunities, and access to capital. Investors, customers, employees, suppliers, and other stakeholders are asking companies for data and reporting on their sustainability outcomes, and companies are now using that data to guide operational and strategy decisions. According to McKinsey & Co, corporations will need to focus on sustainable and inclusive growth that adapts to climate-related risks, competes effectively for limited resources, and shares costs and benefits across generations. This course highlights what all MBA students should know about sustainability as it will continue to affect all organizations - global public corporations, small private companies, large nonprofits like hospitals and universities, and government entities. This course will focus on the major issues and challenges of sustainability, the ESG framework and measurement challenges, and practical examples of how sustainability is driving operations and strategy in different industries. Of course, not all industries and corporations are in the same place along this spectrum, and stakeholders can vary in importance and relative power. But all companies should understand sustainability basics to protect and enhance their financial performance and to inform decision-making, and reduce negative externalities like poor water and air quality, greenhouse gas emissions, and social harm.
MBA 4360 Introduction to Data Mining (2 Credits)
Develop an understanding of more complex concepts of probability and statistics, and how they relate to managerial type problems and decision making. Develop experience performing and interpreting complex analysis methodologies. Obtain further familiarity with statistical software packages. Prerequisite: MBA 4160.
STAT 4350 Statistical Computing (4 Credits)
Introduction to and training in the use of modern statistical software packages. Exposure to several of SAS, STATISTICA, S-PLUS, and SPSS with focus on one to best fit student needs. Data acquisition, management, graphs, analyses, reports, customizing and programming. Cross listed with STAT 3350.
STAT 4700 Intro Computer Simulation (4 Credits)
Deterministic and probabilistic model structures, planning models, heuristics and artificial intelligence, Monte Carlo methods, simulationprogramming languages, model design, experimentation, and verification. Prerequisite: MBA 4111, MBA 4112, or permission of instructor.
STAT 4709 Computer Simulation Methods for Business (4 Credits)
Large-scale simulation in business and economics, deterministic and probabilistic model structures, corporate planning models, heuristics and artificial intelligence; Monte Carlo methods, model design, experimentation and verification, tactical problems in total systems simulation. Cross listed with STAT 3709.
STAT 4710 Statistical Quality Control (4 Credits)
Applies the basic concepts of statistics to quality improvement in the business environment. Topics include a summary of Total Quality Management (TQM) and where Statistical Quality Control fits in, the tools of Statistical Process Control, Deming's Continuous Improvement Cycle, as well as the evaluation of Process Capability and Sampling. Cross listed with STAT 3710. Prerequisite: MBA 4111, MBA 4112, or permission of instructor.
STAT 4810 Nonparametric Statistics (4 Credits)
Statistical procedures applicable in many situations where standard normal theory methods are not. Especially useful when data are of categorical or rank type or when sampled population is excessively skewed. Emphasis will be on applications, making use of the laws of probability. Cross listed with STAT 3110. Prerequisite: MBA 4111, MBA 4112, or permission of instructor.
STAT 4860 Operations Research II (4 Credits)
Non-linear models and optimization, Kuhn Tucker conditions, quadratic and dynamic programming, inventory and queuing models, simulation. Permission of instructor required.
CPSY 4753 Applied Sport Coaching 3: Data Collection and Analysis (1 Credit)
Applied Sport Coaching 3 exposes students to issues and methods pertaining to data collection and analysis. Also, students reconcile issues related to the research process and make decisions that focus the scope of the research. Students further their relationships with key stakeholders and, if not already, obtain institutional review board approval prior to data collection. With stakeholders, students solidify the framing of the problem to be addressed and use data collection and analysis methods to understand the problem.
INTS 4051 Statistical Methods II (4 Credits)
This course is a continuation of Statistical Methods I, covering the fundamentals and primary methods of statistical inference. Topics include two-sample hypothesis testing, analysis of variance, chi-square goodness-of-fit tests, chi-square contingency analysis, correlation, simple regression and multiple regression. Emphasis will be on problem solving, computer applications (using Stata) and interpretation of results. This course is offered in the Winter quarter only. Prerequisites: INTS 4050.
This course serves as an introduction to time-series analysis and panel data analysis techniques. Topics include moving averages, exponential smoothing, time-series decomposition, model identification and estimation, Box-Jenkins method, ARMA and ARIMA models, and VAR analysis. Panel data analysis includes fixed effects and random effects models. Emphasis will be on computer applications (using Stata) and interpretation of results.
INTS 4632 Qualitative Research Methods (4 Credits)
This course provides training in ethnographic and engaged research methods while giving students the opportunity to apply their skills to the local Denver immigrant community. This class requires a commitment to doing fieldwork outside of the classroom and to organizational partners in the community. Students should expect to spend 3-4 hours a week in the field and 1-2 hours on their field note write-ups. Students will work on the Wage Theft in the Denver Construction Industry project being led by Professor Galemba in collaboration with El Centro Humanitario, a day laborer center in Denver. Or they may choose projects with Casa de Paz and the Colorado Immigrant Rights Coalition. Students will gain experience with participant observation, qualitative interviews, data security protections, qualitative data coding, analysis, reflexivity and positionality in research, and writing. The course culminates in a public presentation to share results with the community. Spanish skills are a plus, but are not required for all students.
INTS 4739 Defense and Security Quantitative Analysis (4 Credits)
This course is the follow-on to INTS 4735 Defense and Security Methods and is designed to engage students in a professional conversation about the applicability of quantitative analysis and big data based analytics for the execution of defense and security analysis/research. Continuing the development of the students' individual research design proposal, but now introducing an array of quantitative ideas, options, and methods, this course begins with the foundational realities of coding and descriptive statistics before introducing students to bivariate and multivariate analysis, index/scale construction, and hypothesis testing techniques. In addition, the course continues to develop the students ability to engage with and understand real world defense and security research, in this case particularly quantitative analysis. Prerequisites: INTS 4735.
PPOL 4400 Introduction to Policy Analysis (4 Credits)
This course will provide the student with the analytical tools necessary to evaluate competing points of view, using empirical techniques, logic, and statistical inference. Case studies will be drawn from the current legislative and regulatory environment and will provide the MPP student with opportunities to construct a course of action, based on the use of logically consistent arguments and on the persuasive use of facts and empirical data. Students in this course will also learn the history and development of the scientific method, how to distinguish speculation, theory, fact, and opinion, how to identify the validity, ideological content or irrationality of data, how to identify the intentional obfuscation of issues, and how to evaluate one's own prejudices and vulnerability to argument not based on evidence. Students in this course how to identify the validity, ideological content or irrationality of information, how to identify the intentional obfuscation of issues, and how to evaluate one's own prejudices and vulnerability to arguments not based on evidence.
PPOL 4823 Executive Data Analysis and Visualization (4 Credits)
This course is aimed at helping policy professionals to review basic principles of statistics and apply this knowledge to visualizing data for policy analysis and communication. Students will use software to produce visualizations for effective and accessible data description and analysis.
RMS 4915 Hierarchical Linear Modeling (4 Credits)
This course introduces models that extend multiple regression to analysis of nested data structures common in education and other social sciences. Application of those methods to various forms of multilevel data, including repeated measure (growth trajectory) data is emphasized. Prerequisite: RMS 4911.
RMS 4941 Introduction to Qualitative Research (4 Credits)
This course is designed to provide students with more in-depth understanding of naturalistic, qualitative research methods. It is assumed that students enrolling in this course have already completed an introductory research methods course in either education or another discipline. Purposes and questions posed in their course include: Why should a researcher choose to conduct a qualitative study? How are data collection strategies carried out in a qualitative research design? What are some of the ethical concerns that impact qualitative research?.
BIOL 4095 Research Methods and Analysis (4 Credits)
The purpose of this course is to introduce you to topics of applying statistical knowledge to real data, including specific tests/models as well as issues related to project design such as adequate sample size, avoiding confounding variables, unexplained error, and other common challenges. It is geared toward both field and lab-based projects, but the topics covered are relevant to research generally. Each week we will discuss the reading for that week with the objective of clarifying points and where possible, applying the concepts to real data from our own work. Prerequisites: BIOL 4090 or permission of instructor. Prerequisites: BIOL 4090.
GEOG 3160 Web GIS (4 Credits)
With the development of internet technologies, the architecture of Geographic Information System (GIS) has evolved from the centralized desktop architecture to the distributed web architecture. Numerous web GIS applications are available (e.g., Google Map, Earth Explorer, and National Map). A web GIS application allows GIS analysts to access, manipulate, and visualize geospatial data from the web without the installation of GIS software. To facilitate the development of web GIS applications, geospatial technology vendors have provided application programming interfaces (APIs) through which GIS professionals can build customized web applications. This course focuses on the concepts and the development of web based GIS applications using industry-relevant geospatial APIs and core web technologies of HTML, CSS, and JavaScript. This is an upper-level undergraduate, to graduate-level course in GIS that introduces fundamental Web GIS concepts, applications and development kits. Concepts and techniques to be covered in this course include: • Web GIS concepts: system architecture, components, and workflow • Web programming languages: Hypertext Transfer Markup Language (HTML), Cascading Style Sheet (CSS) and JavaScript • Web mapping tools: ArcGIS online, Leaflet and their APIs. Prerequisites: GEOG 2100 and GEOG 3130.
PHYS 3252 Astrophysics: Observations (4 Credits)
Astronomy is fundamentally an observational science and as such it is important for practitioners to understand how their data are collected and analyzed. This course is therefore a comprehensive review of current observational techniques and instruments, aimed at advanced undergraduates, as well as graduate students focusing on astrophysics research. This class introduces students to the capabilities and limitations of different types of instruments while exploring the sources and types of noise and providing statistical tools necessary for interpreting observational data. Credit can apply toward physics or astrophysics minor. Prerequisites: PHYS 2252 and MATH 1953, or instructor's permission.
ANTH 3640 Race and Human Evolution (4 Credits)
Examines the history of thought about the nature and evolution of human racial differences and sexual characteristics, from the mid-19th century to the present day. Considers scientific and poplar models for explaining the evolution of racial differences, male-female reproductive behavior and gender roles. These models are examined in light of comparative primate data, ethnographic data and the material record of human evolution. Prerequisite: ANTH 2010.
ECON 4670 Econometrics: Multivariate Regression Analysis for Economists (4 Credits)
This course develops the theoretical foundations of ordinary least squares (OLS) regression analysis and teaches students how to specify, estimate, and interpret multivariate regression models. Students have to apply what they have learned using a popular software package used for econometrics and real data. Special topics also covered include regression models that include dummy variables, log-linear models, fixed effects models, a brief discussion of instrumental variables, and an introduction to time-series analysis and forecasting. Cross-listed with ECON 3670.
EDPX 4200 Data Visualization (4 Credits)
This course explores the creation of informational graphics for visual unpacking of relationships within and among data sets. Students learn to visualize large data sets as a means of revealing and exploring patterns of information. Creating interactive visualizations is also covered, allowing for deep and participatory engagement with information. The resulting mediums include print and web. Lab fee. Cross listed with EDPX 3200. Prerequisites: EDPX 4000 and EDPX 4010 or permission of the instructor.
EDPX 4320 Interactive Art (4 Credits)
This course expands the concepts, aesthetics, and techniques critical to the exploration and authoring of interactive art. It explores human computer interactions; user/audience interface design/development; interactive logic, author-audience dialogue; meta-data/multimedia asset acquisition and authoring environments. While utilizing students’ skills in numerous media forms, the class focuses on sensing, interactive scripting techniques, and emerging forms of digital narrative. Emphasis is on the development of interactive media deployment and distributions ranging from screen media to physical environments. Lab fee. Cross listed with EDPX 3320. Prerequisites EDPX 4310 or EDPX 4450.
EDPX 4450 Visual Programming (4 Credits)
This course introduces intuitive visual "programming" that allows rapid building of personalized tools for data, video, image, and sound manipulation. These tools can be used in real-time editing or performance, complex effects processing, or to bridge between multiple pieces of software. Lab fee. Cross listed with EDPX 3450. Prerequisite: EDPX 4010 or permission of the instructor.
EDPX 4460 Visual Programming II (4 Credits)
This class uses advanced visual programming concepts (as provided by Max/MSP and Jitter) to explore visualization and sonification techniques in an artistic context. Areas of exploration include OpenGL modeling and animation, virtual physics emulation, audio synthesis techniques, and external data manipulation. Students use these concepts to create art installation and performance projects. Lab fee. Cross listed with EDPX 3460. Prerequisite: EDPX 4450.
PSYC 4300 Correlation and Regression (4 Credits)
The course reviews the logic of statistical inference before introducing the procedures of correlation and regression. We begin with simple bivariate relationships before moving on to multivariate relationships for both categorical and continuous independent variables. Topics in regression include multicollinearity, variable selection, and curvilinear relationships. The course emphasizes the (stringent) requirements needed to be able to interpret correlational data in terms of cause and effect. The course also emphasizes the assessment of interactions in regression analysis for both categorical and continuous independent variables. Also included is basic coverage of logistic regression and regression assumptions. Prerequisite: PSYC 4295.
PSYC 4350 Structural Equation Modeling for the Social Sciences (4 Credits)
This advanced course covers the basics of structural equation modeling and how this flexible approach to statistical analysis can be applied in the social sciences. Specific techniques that will covered will include testing for mediation, path analysis, confirmatory factor analysis, and the analysis of longitudinal data, as well as other related topics. There will be an emphasis on applying these techniques to students' own research through hands-on demonstrations and homework assignments and an emphasis on interpreting and critiquing structural equation models in published research. A course on correlational methods and regression is a pre/co-requisite.
COMP 3008 Foundations in Data Science Mathematics II (4 Credits)
This course presents the elements of linear algebra and discrete math essential for subsequent coursework in data science.
COMP 3424 NoSQL Databases (4 Credits)
In this course, students learn what NoSQL databases are, learn to identify the differences between them, and gain a fundamental understanding between SQL, relational databases, and NoSQL databases. Students further explore which type of NoSQL database is the correct one given a use-cases, examining types, methods of communicating with it, contrasts to other NoSQL databases, performance and scalability. Prerequisites: for undergraduates, COMP 2355 or COMP 2361; for graduates: COMP 3005.
COMP 3621 Computer Networking (4 Credits)
An introduction to computer networks with an emphasis on Internet protocols. Topics include: internet design, application layer protocols such as SMTP and HTTP, session layer protocols including TCP and UDP, the internet protocol (IP), link layer technology such as Ethernet, and security issues related to networking. Programming experience of client/server architectures using sockets and TCP/UDP through projects is emphasized. Prerequisites: for undergraduates: (COMP 2355 or COMP 2361) and COMP 2370; for graduates COMP 3004 and COMP 3006. Cross listed with COMP 4621.
COMP 3731 Computer Forensics (4 Credits)
Computer Forensics involves the examination of information contained in digital media with the aim of recovering and analyzing latent evidence. This course will provide students an understanding of the basic concepts in preservation, identification, extraction and validation of forensic evidence in a computer system. The course covers many systems level concepts such as disk partitions, file systems, system artifacts in multiple operating systems, file formats, email transfers, and network layers, among others. Students work extensively on raw images of memory and disks, and in the process, build components commonly seen as features of commercial forensics tools (e.g. file system carver, memory analyzer, file carver, and steganalysis). Prerequisites: COMP 3361; COMP 2355 or 2361 for undergraduates; COMP 3006 for graduates.
Fundamentals of 3D rendering including the mathematics behind coordinate systems, projections, clipping, hidden surface removal, shadows, lighting models, shading models, and mapping techniques. Significant use of 3D APIs through shader programming is covered along with the basics of 3D model representation and animations. Satisfies "Advanced Programming" requirements for graduate students. Prerequisites: COMP 2370, MATH 1952 or 1962.
COMP 3904 Internship/Co-Op in Computing (0-10 Credits)
Practical experience in designing, writing and/or maintaining substantial computer programs under supervision of staff of University Computing and Information Resources Center. Prerequisites: COMP 2370 and approval of internship committee (see department office).
COMP 4432 Machine Learning (4 Credits)
This course will give an overview of machine learning techniques, their strengths and weaknesses, and the problems they are designed to solve. This will include the broad differences between supervised/unsupervised and reinforcement learning as well as associated learning problems such as classification and regression. Techniques covered, at the discretion of the instructor, may include approaches such as linear and logistic regression, neural networks, support vector machines, kNN, decision trees, random forests, Naive Bayes, EM, k-Means, and PCA. After course completion, students will have a working knowledge of these approaches and experience applying them to learning problems. Enforced Prerequisites: COMP 4442 and COMP 4581.
COMP 4449 Data Science Capstone (4 Credits)
Students identify and fill a demand for an innovative data science product, such as a data base tool, analytical software, or domain specific analysis. The product is defined, implemented, documented, tested, and presented by the student or student team with the instructor and other stakeholders acting as a project supervisors to verify that goals are met through the 10-week development process. Prerequisites : COMP 4442, COMP 4448, and COMP 4581.
COMP 4455 Shell Scripting and System Tools (4 Credits)
This course covers navigating and utilizing tools in a UNIX environment, including use of common command line utilities, Bash and Python shell scripting, source control via Git, pipes and I/O redirection, networking in Python and OS multi-processing/multi-threading. More emphasis will be placed on using these tools than on how those tools work. Prerequisite: COMP 3006.
COMP 4600 Seminar in Computer Science (0-4 Credits)
Preparation and presentation of lectures on some aspect of current research in computer science; topics not generally encountered in formal courses, may include robotics, pattern recognition, parallel processing, computer applications. 10- to 15- page paper with bibliography required.
COMP 4621 Computer Networking (4 Credits)
The Internet is arguably the most transformative invention in recent history and is at its core a massive global computer network (of networks). Students in this course learn how the Internet works, from the highest-level application layer to the lowest-level hardware layer. Topics covered include the OSI and TCP/IP reference models, physical transmission methods, error detection and correction, addressing, routing algorithms, congestion control and more. Prerequisites: COMP 3006, COMP 3361 (or instructor approval). Cross listed with COMP 3621.
COMP 4721 Computer Security (4 Credits)
This course gives students an overview of computer and system security along with some cryptography. Some network security concepts are also included. Other concepts include coverage of risks and vulnerabilities, policy formation, controls and protection methods, role-based access controls, database security, authentication technologies, host-based and network-based security issues. Prerequisites: 3006, COMP 3361 (or advisor/instructor approval).
ENCE 3250 HDL Modeling & Synthesis (3 Credits)
This course is an introduction to the basic concepts in image processing and computer vision. First, an introduction to low-level image analysis methods, including radiometry and geometric image formation, edge detection, feature detection, and image segmentation are presented. Then, geometric-based image transformations (e.g., image warping and morphing) for image synthesis will be presented in the course. Furthermore, methods for reconstructing three-dimensional scenes including camera calibration, Epipolar geometry, and stereo feature matching are introduced. Other important topics include optical flow, shape from shading, and three-dimensional object recognition. In conclusion, students learn and practice image processing and computer vision techniques that can be used in other areas such as robotics, pattern recognition, and sensor networks. Cross listed with ENCE 4620. Prerequisite: ENEE 3111.
ENCE 4231 Embedded Systems Programming (4 Credits)
This course covers advanced concepts in image processing and computer vision including but not limited to image radiometry and geometric formation, edge detection, geometric based transformations (e.g., image warping and morphing), camera calibration, Epipolar geometry, and stereo feature matching. Other advanced topics include optical flow, shape from shading, and three-dimensional object recognition. In conclusion, students learn and practice advanced topics in image processing and computer vision techniques that can be used in other areas such as robotics, pattern recognition, and sensor networks. Cross listed with ENCE 3620. Prerequisite: ENEE 3311.
This course is the capstone mechanical engineering laboratory course requiring independent experimental design by student teams. Using experimental equipment available in heat transfer, fluid mechanics, solid mechanics, thermodynamics, and measurement and control, the student team is required to design experiments to solve given problems which will be unique to each team. This course encourages students to develop experimental design and research techniques while continuing to improve skills in fundamental lab notebook keeping, uncertainty analysis in measurements, data acquisition, data analysis, report writing, oral presentations, and laboratory safety and procedures. Prerequisite: ENME 2810.
ENME 4530 Advanced Dynamics (4 Credits)
Formulation of equations of motion for constrained 3D multibody systems with: D’Alembert principle (MG road-maps); power, work, and energy; Lagrange’s equations; and Kane’s method. Euler parameters/quaternions, specified motion, constraint force/torque calculations, feed- forward control, inequality constraints and/or intermittent contact. Tensors and mass property calculations. Symbolic and numerical computer skills for geometry/kinematic analysis, mass/inertia calculations, forces and motion, and simulation of multi-body dynamic systems. Training for advanced research and professional work. Recommended pre-requisite: ENME 4520.
ENGR 3650 Probability and Statistics for Engineers (4 Credits)
This course covers quantitative analysis of uncertainty and decision analysis in engineering. It covers the fundamentals of sample space, probability, random variables (discrete and continuous), joint and marginal distributions, random sampling and point estimation of parameters. It also covers statistical intervals, hypotheses testing and simple linear regression. The course includes applications appropriate to the discipline. Prerequisite: MATH 1953.
ENGR 4503 Graduate Capstone Design III (3 Credits)
This is a project-centered course. This is the third of a practical class that implements the engineering design process (right side of the vee). This requires teamwork to build, checkout, and test the final product. In this segment, the engineering teams build or procure hardware as a step towards the integration of the system. This class puts theory into practice by building components, developing software modules, integrating software with hardware, checkout of the system, and performing tests to verify construction, validate models, and collect data for acceptance by the team prior to demonstrating the operations of the product to the customer. Test data is collected through instrumentation of the final product with a buy-out and certification by the team. Testing may include performance testing and environmental testing as envisioned in the context diagram.
ENGR 4504 Graduate Capstone Design IV (3 Credits)
This is a project-centered course. This is the fourth of a practical class that implements the entire engineering “vee” design process. This requires teamwork to build, checkout, and test the final design product, e.g. hypothetical missile. In this segment, the engineering teams fine-tune the design process which may address advanced topics such as fault management and resilience. This class puts theory into practice by building components, developing software modules, integrating software with hardware, checkout of the system, and performing tests to verify construction, validate models, and collect data for acceptance by the team prior to demonstrating the operations of the product to the customer. It may also include addressing the beginning of the program through early management and pre-phase A activities. Test data is collected through instrumentation of the final product with a buy-in and certification by the team. Testing may include performance testing, functional testing, and environmental testing as envisioned in the system process.
INFO 4140 Business Databases (4 Credits)
This is an introductory database course which covers enterprise database design, modeling and implementation.
INFO 4340 Data Mining and Visualization (4 Credits)
In this course, students create business intelligence tools such as balanced scorecards, data visualization and dashboards to inform business decisions. The course will focus on the identification of metrics, measures, and key performance indicators for a variety of business operations, and will introduce numerous analytic methodologies to support the decisions made with regard to these metrics. The focus will be on the advantages and disadvantages of various modeling methodologies and implementations moving towards performance improvement and business understanding.
Prerequisite: STAT 4610.
BUS 4138 Artificial Intelligence (2 Credits)
Hollywood has taught us that artificial intelligence (AI) involves robots that want to take over the world in some fashion. While this notion is both terrifying and fascinating to watch, it fails to portray how AI is being used successfully by businesses to create value for stakeholders. This course will help inform your perspective on how AI is helping businesses by giving you insight into how its currently being used by businesses. Included in this is developing your understanding of how organizations develop their AI capabilities, a look at various AI techniques including machine learning, and a discussion on the ethical challenges raised by using AI in business. The ability to understand and use AI in business could transform the way business is done by equipping organizations with the ability to reimagine what is possible and simultaneously deliver it.
BUS 4144 Blockchain (2 Credits)
In this course, students will understand how blockchains operate as decentralized ledgers and how businesses can begin to leverage the opportunities these types of technologies afford to them. With an understanding of blockchain, the course will transition to cryptocurrency and discuss the emerging values proposition these new types of currencies provide to businesses. From digital currencies, the course will instruct students on the emerging concept of non-fungible tokens (think digital art) and will conclude with an investigation into the ethical challenges related to blockchain technology.
BUS 6004 Data Analytics (4 Credits)
The main objective of this course is to provide students with a well- grounded understanding and appreciation of the contemporary methods, tools and techniques used to make evidence-based managerial decisions. As managers and practitioners in business, industry and government, you have made substantial investments in putting in place the means to collect and store data, but may not have the basic technical or analytical understanding necessary to chart a road map to discover the full potential of your data. This course intends to provide you with such an understanding and hence help you become a better manager/decision maker.
BUS 6400 Academic Skills for Doctoral Students in Business (2 Credits)
The first purpose of this course is to prepare doctoral students with skills and knowledge that are not commonly part of the course requirements but are imperative for a successful navigation of the job market and a successful publication career. This includes managing the peer review process, navigating a conference to extract the most value, and managing co-author relationships. The second purpose of the course is to provide students with just-in-time data-analysis skills based on their identified path of research interest. Archival research in business requires a significantly different skill-set than behavioral research. Students will complete one of two tracks, archival or behavioral, in the form of an intensive two-day workshop where they utilize actual data to replicate a published research study.
FIN 4000 Financial Modeling and Databases Bootcamp (1 Credit)
This bootcamp is designed to introduce students to financial databases and to familiarize them with basic financial data analysis using Excel. The goal is for students to become comfortable with platforms such as Capital IQ and WRDS, which they will be using throughout their academic and/or professional careers. In addition, students will acquire a basic command of Excel functionality and efficiency in data analysis, together with modeling best practices and practical finance applications.
MGMT 4410 Qualitative Research Methods (2 Credits)
This course provides students with an overview of and experience with qualitative methods. You are introduced to a wide variety of qualitative methods, including ethnography, observation, interviewing, grounded theory, discourse analysis, deconstruction, historical methods, and action research. The course is roughly divided into two major sections. The first half of the course introduces you to the epistemological foundations of qualitative research and emphasizes design and data collection. The second half of the course introduces a variety of techniques for coding and analyzing qualitative data and provides exposure to many exemplars of qualitative reports/studies. We will examine conventions for ensuring that qualitative work is rigorous and appropriate for action. Throughout the course you will be given opportunities to try on various methods and gain some hands-on experience in several areas.
MGMT 4530 Technologies for Sport & Entertainment Management (2 Credits)
This is a specialized course for the MBA student interested in expanding their knowledge of the sports industry as a business and as a world economic force. It provides students with a framework for understanding the scope of the sports business across various venues, as it relates to information technology. Management Sport Technology focuses on understanding the practical uses of computer applications as a tool in sport management activities. Emphasis is placed on demonstrated proficiency in project management, spreadsheet management, database management, and Web page development.
MKTG 4835 Search Engine Marketing: Google Analytics & Google Ads (4 Credits)
An understanding of consumers' search behavior provides deep insight into how people make purchasing decisions and form brand affinities. Search marketing is also the cornerstone of many digital marketing campaigns. This course provides a comprehensive foundation in search marketing and digital analytics as tools for any marketer, as well as hands-on experience with Google Ads and Google Analytics. You will be working with real-world clients, helping them to drive marketing ROI! Cross listed with MKTG 3485.
ACTG 4530 Business Advisory and Internal Audit (4 Credits)
In today’s business environment, a given company’s “internal audit” function is asked to not only help keep the business out of trouble (control risks), but also help make the business better (leverage risk management to make better business decisions). In this course, we will “reimagine” internal audit with a heavy focus on the business advisory aspects of audit. Topics specifically covered in this course include risk appetite and tolerance, risk culture and an array of cutting-edge audit and advisory topics (cybersecurity, data privacy, social media, to name a few). Students experience “real life” examples and case studies to truly experience the role of an auditor as business advisor. Prerequisite: ACTG 3551 or ACTG 4551.
This course has two primary objectives. The first is to expose students in a coherent way to current exploding new technologies that create possibilities and challenges for businesses, organizations, and individuals alike. Key 4th industrial revolution technologies such as artificial intelligence (AI), the Internet of Things (IoT), Blockchain technology and cryptocurrency, extended reality (augmented, mixed, and virtual), additive manufacturing, and autonomous vehicles and drones are also introduced. The second objective is to have students digest and think through what these technologies mean for the organizations they do and will lead in the future. Notably, what does it mean to lead an organization and be a well informed customer/user of technology without being a technology expert themselves. In essence, how do you lead a 21st century enterprise that is increasingly shaped by 4th Industrial Revolution technologies?.
ITEC 4481 C#.NET (4 Credits)
The goal of this course is to provide students with the knowledge and skills needed to develop C# applications for the Microsoft .NET Platform. The course focuses on C# program structure, language syntax, and implementation details. C# was created to be the programming language best suited for writing .NET enterprise applications. C# combines the high productivity of Microsoft Visual Basic with the raw power of C++. It is a simple, object-oriented, and type-safe programming language that is based on the C and C++ family of languages.
ITEC 4610 IT Strategy (4 Credits)
Businesses run on information, organized data about customers, markets, competition, and environments. Information systems (interconnected computers, data, people, and processes) are critical to capture, organize, and disseminate that information in ways that provide stakeholder value. This course is designed to help managers, technical and non-technical alike, to explore how to derive greater value and satisfaction, both personally and professionally, from information systems.
STAT 4850 Operations Research I (4 Credits)
Linear programming, including transportation, warehousing, assignment models, and sensitivity analysis, integer programming and game theory. Permission of instructor required.
RLGN 4645 Artificial Intelligence and What It Means to Be Human (4 Credits)
Artificial Intelligence raises pressing questions about machines: Are they really intelligent? Can they have consciousness? Ought they have moral status? Are algorithms related to computers like minds are to bodies? Do smart machines change the relationship of humans to technology? Each of these questions, in turn, is actually a question about human nature: What are the kinds of human intelligence, and are they unique to humans? Why do humans have moral status? What kinds of embodiment are essential to humans? (Do we include things like race and gender?) Are humans tool-users, or did we evolve as humans because of tools? In that case, have humans always been cyborgs? Questions about human nature are one of the classic theological loci, falling under the rubric of theological anthropology. In other words, religious traditions have thousands of years of deep thinking on these questions that are being raised in new ways (as Nick Bostrom has famously argued, AI is like “philosophy with a deadline”). This course is a sophisticated but non-technical introduction to the history of AI and to the tools and ideas of AI in its current forms. We will cover the most important ethical issues with which AI confronts us, and bring the resources of philosophy and theology to tackling some of the questions of human nature raised by AI.
CPSY 4010 Introduction to Statistics (3 Credits)
General statistical principles and techniques and their application to psychological and psycho-legal issues. Students will develop computer analytic skills to assist in answering professionally relevant questions.
CPSY 4735 Understanding Sport Research (4 Credits)
Graduate level couse to educate students on understanding and doing sport research. The primary focus of this course is on facilitating student's understanding of research methods commonly used in sport research. Secondarily, the course will examine how research is actually done, including reviewing the literature and writing and referencing scholarly work. Course content will cover topics such as paradigms and philosophy of science, epistemology and the creation of knowledge, and numerous research designs, methodologies and methods. Content will also include understanding statistics and qualitative methods.
INTS 4438 International Public Opinion and Foreign Policy (4 Credits)
This course examines international public opinion and introduces the major international opinion trends that impact foreign affairs. The course first reviews international public opinion worldwide, then by major regions and finally a selection of leading countries. The theoretical question is how public opinion influences foreign policy in countries around the world, and if and when it does, under what conditions. Also, how international opinion affects American foreign policy, including the views of foreign publics toward America and its policies, is also examined. The course begins with an investigation of the history of collection and diffusion of international survey research, the quality of the data and the techniques used to collect it. The relationship of public opinion research and democratic government and media freedom is examined. The second part of the course outlines some of the public opinion benchmarks, their variations and similarities among countries and regions, and their change over time. A variety of the best sources of opinion data are used. Benchmarks include: level of satisfaction with the direction of own nation; satisfaction with and preferences for form of government; satisfaction with and preferences for economic system, the role of government intervention and entrepreneurial values; nationalism and approach to neighbors; attitudes toward Americans, American leadership and foreign policy; and impact of cell phones and Internet on opinion formation and collection. The course's orientation is both from an American foreign policy perspective and from the perspective of key international organizations, such as the UN, OAS, EU, etc. At the conclusion of the course, students should be familiar with the history and sources of international public opinion research, the major similarities and differences in international and regional public opinion, and the impact that it has on both American and international, multinational organization foreign policy decision-making. When available, there are guest speakers concerning the impact of public opinion on foreign policy decision makers.
INTS 4633 Int'l Project Evaluation (4 Credits)
It can be beneficial for graduate students planning careers in multilateral and bilateral development agencies, non-profit organizations, private-sector companies, and professional services organizations to have an understanding of how to develop a project proposal, implement it, and evaluate its results. These are useful skills for entering or reentering employment with these organizations. The Josef Korbel School of International Studies currently offers a trilogy of courses in international project cycle management—international project design and monitoring, project management, and international project evaluation. The three courses are delivered in sequence during the academic year in conformance with the project cycle, but they can be taken out of sequence without prerequisite or need to take them all. Each course uses monitoring and evaluation methods and means to connect the design, management, and evaluation of a project. Students may have been exposed some of these methods in courses covering quantitative and qualitative techniques and field research methods. Each course also shares in common the development teams and managers of those teams to produce the key deliverables at three key stages of the international project cycle. The purpose of the International Project Evaluation course is to provide students with a better understanding of and practical tools for designing, implementing, and reporting project evaluations. In all cases, a good evaluation design that is well implemented will allow the project manager to identify supportable findings, conclusions, and recommendations. The recommendations from both performance and impact evaluations can be directed to decision makers to support changes necessary to correct project deficiencies or to provide lessons learned for designing subsequent development interventions. Project managers can also use community or stakeholder participation in the process to build evaluation capacity and to gain support for the results. More specifically, students will learn about similar approaches used by four organizations that evaluate project, programs, and policies—the U.S. Government Accountability Office, the World Bank, United Nations Development Program, and United States Agency for International Development (USAID). Each of these organizations has developed templates for evaluation design, use similar methods and techniques to collection and analyze data, and share common elements in the framework of their evaluation reports. Two of these organizations have protocols to contract out evaluations to other groups through the preparation of an evaluation statement of work (SOW) or terms of reference (TOR). In this course, students will have the opportunity to compare evaluation approaches and to apply these approaches in preparing evaluation products. Small student teams will produce an evaluation SOW patterned after USAID guidance and defend their design in a final presentation.
CNP 4730 Research Methods and Program Evaluation (5 Credits)
This course is designed to provide an introduction and overview of comprehensive program development and evaluation, and research methods. The course will provide direction on the following topics: causation, research hypotheses, independent and dependent variables, sampling, internal and external validity, experimental, quasi-experimental, single-subject, causal-comparative, and correlational designs, measurement and data collection procedures, types of instrumentation and methods for determining reliability.
ADMN 4849 Action Research for School Leaders (4 Credits)
This course emphasizes the use of research methods which are linked to research needed in schools. Students will learn to identify, analyze and solve problems. Some of the action research methods include focus groups, interviews, observations, school records and surveys. Capstone project will relate directly to the improvement of school policy and practice.
ADMN 5910 Dissertation Seminar for Educational Leadership and Policy Studies (2 Credits)
This course is designed as a workshop to support students in the ongoing development of the EdD Dissertation in Practice (DiP) or their PhD dissertation, to work collaboratively to finalize the literature review, research plan, and/or data analysis. Our work together will be highly interactive. Students are expected to work closely with their chairs /dissertation directors/advisors and other committee members throughout the process. Feedback from the instructor will in no way supersede the judgment of the chair/dissertation director or committee members. The purpose of this course is to guide students in completing the DiP or the dissertation. This is not a research methods course, but a doctoral dissertation course focused on the application of research understandings, knowledge, concepts, and terminology in the design of a dissertation. It is assumed that prerequisite research courses provide students with considerable information, foundational knowledge, and conceptual understandings of both quantitative and qualitative research methodologies and other relevant topics.
LIS 4011 Information Access & Retrieval (3 Credits)
Information retrieval is defined as the process of searching for (and retrieving) relevant information within a document collection. The document collection could be textual (bibliographic records), structured and unstructured data, library databases, web based information resources, multimedia resources, and numerical data. This course introduces students to important access and retrieval tools and technologies used to retrieve information that are relevant to a user's information need. In addition to the underlying principles and processes revolving around access and retrieval such as text operations, indexing, query languages, and searching, the course covers relevant topics such as library discovery systems, web based information retrieval technologies, and enterprise search systems.
LIS 4355 The Reading Experience in LIS (3 Credits)
Working with readers has always been a cornerstone of library practice. Traditionally referred to as readers’ advisory, reading work has expanded beyond book displays, individual requests for a good read, and book clubs. It has become a foundation of extensive library programming, meaningful leisure, and personal enjoyment, and an instrument of building healthy and engaged communities; it has turned from monolingual to multilingual; and it has claimed its place in the virtual library environment. No longer limited to public libraries, reading work comprises a growing area of interest in academic and special libraries, book publishing, and book trade. This course will introduce students to the selected theories of reading behaviors; practical skills of engaging readers, developing reader services, programming, and advocacy; the major genres and sub-genres of fiction and nonfiction materials; a wide array of print and electronic tools facilitating this practice; electronic reading and online reading communities; and foundations of bibliotherapy. This course is focused on adult readers. For other audiences, see LIS 4510 Children’s Materials & Services & LIS 4520 Young Adult Materials & Services.
RMS 4910 Introductory Statistics (4 Credits)
This beginning statistics course examines use and interpretation of statistics in educational and human services research, including descriptive and inferential techniques. Cross listed with SOWK 5930.
RMS 4931 Survey and Design Analysis (3 Credits)
Survey techniques, needs assessment, item construction, sampling, maximizing response rates and data analysis; survey construction and data analysis are required. Prerequisite: RMS 4910.
RMS 4932 Meta-Analysis Social Science Research (3 Credits)
This course examines meta analytic techniques in the social sciences. Included are discussions of review of critical data bases, coverage of all major methods of data collection and analysis, and coverage of how best to present meta analytic findings for publication. Prerequisite: RMS 4911, RMS 4930, and preferred RMS 4912.
CFSP 4351 School Psychology Practicum: Clinic Assignment (1-3 Credits)
CFSP Clinic is a supervised field experience in the Morgridge College of Education’s Counseling and Educational Services Clinic. Through all experiences, Clinic students will work with students and families within the zero to college age range. Casework may include: interview, assessment, data analysis, report writing for different audiences, diagnostics, data presentation, intervention, and consultation for a variety of psychoeducational and developmental concerns of children and families. Prerequisites: All prior first year courses as listed in the handbook, CFSP 4351 - prior quarters.
CUI 5981 Research as Intervention (3 Credits)
This course is the second of three culminating research courses for students in the Ed.D. in Curriculum and Instruction and is designed to help candidates finish collecting their data and analyze their data. This course will also introduce and develop the evaluation or analysis section of the doctoral paper and the beginning steps of the dissemination of the research project.
Statistic on biological research; emphasis on procedures, applications of regression, correlation, analysis of variance, and nonparametric tests. Include instruction on computer aided (Mac and PC) statistical analysis and presentation of results. Cross listed with BIOL 2090.
BIOL 4515 Research Techniques in Exercise Physiology (4 Credits)
This graduate level course is designed to provide exposure to several methods of research within the area of exercise physiology. This is a hands-on course that combines theory and literature with practical research experiences in physiology. In this course, students will perform data collection and analysis of differing topics. These may include the following topics: pulse and tissue oxygenation, signaling (heart rate variability, oxygen kinetics), respiratory loops, Doppler ultrasound (blood flow and tissue structure), etc. Our aims will focus on understanding how and why the method works, how to collect data, as well as the analysis and reporting of variables for proper interpretation.
GEOG 3110 GIS Modeling (4 Credits)
This course focuses on the concepts and procedures used in discovering and applying relationships within and among maps. It extends the mapping and geo-query capabilities of GIS to map analysis and construction of spatial models. The course establishes a comprehensive framework that addresses a wide range of applications from natural resources to retail marketing. Topics include the nature of spatial data introduction to spatial statistics and surface modeling in the first five weeks followed by spatial analysis operations and modeling techniques in the second five weeks. The lectures, discussions and independent exercises provide a foundation for creative application of GIS technology in spatial reasoning and decision making.
GEOG 3150 GIS Project Management (4 Credits)
This course provides graduate students seeking a career in GIS, or anyone managing a GIS project, with the knowledge, skill and abilities to take a GIS project or program past the design and implementation phase and into day-to-day operation. Students evaluate and analyze the role of GIS in an organization's overall information system strategy and communicate the importance of geography in an information system. Data sharing in the organization is examined to determine the benefits and costs of distributing data creation and maintenance activities throughout an organization. Finally, the role of GIS professionals and the skill sets required to manage GIS effectively are examined. Students review case studies of successful and not-so-successful GIS projects in North America. GIS management issues are addressed by a series of case studies focusing on various management aspects. Students are also expected to visit operational GIS programs in the metropolitan area and interview GIS managers. Students prepare case study evaluations for review in the classroom. Required for all MSGIS students because of the critical importance of GIS project management.
GEOG 3350 Qualitative Methods in Geography (4 Credits)
This course focuses upon qualitative methods in the production of geographic knowledge. Qualitative methods are widely employed by geographers to understand patterns and underlying processes of human and human-environment issues in society. The course is designed to expose participants to the theories, purpose, scope, and procedures of qualitative research. Specific topics include: epistemological theories (ways of knowing); ethics and power in research; research design; data collection techniques in interviewing, participant observation and landscape interpretation, discourse and archive analysis, and case studies; data analysis; and writing and disseminating qualitative findings.
GEOG 3860 GIS Applications and Natural Resources (4 Credits)
In this course we will use a case study approach to examine domestic and international natural resources such as oil, coal, timber, minerals, and recycled materials. We will use a case study approach to look at resource distribution, and the environmental impacts of extraction, production, and disposal, as well as the legal and economic context. We will use GIS data and analysis to enhance our understanding of these case studies, and students will do a project and paper using GIS data and image analysis at a local, regional or global scale. Prerequisite: Introduction to GIS or Introduction to GIS Modeling.
GEOG 4150 GIS Project Management (4 Credits)
This course provides graduate students seeking a career in GIS, or anyone managing a GIS project, with the knowledge, skill and abilities to take a GIS project or program past the design and implementation phase and into day-to-day operation. Students evaluate and analyze the role of GIS in an organization's overall information system strategy and communicate the importance of geography in an information system. Data sharing in the organization is examined to determine the benefits and costs of distributing data creation and maintenance activities throughout an organization. Finally, the role of GIS professionals and the skill sets required to manage GIS effectively are examined. Students review case studies of successful and not-so-successful GIS projects in North America. GIS management issues are addressed by a series of case studies focusing on various management aspects. Students are also expected to visit operational GIS programs in the metropolitan area and interview GIS managers. Students prepare case study evaluations for review in the classroom. Required for all MSGIS students because of the critical importance of GIS project management.
GEOG 4170 Geospatial Analysis and Project Management (4 Credits)
This course provides an opportunity for students to apply geospatial data analysis to real-world applications. Students will work as a team to develop a project that requires GIS analysis and/or application development, design a project work flow and management plan, and implement a solution. Students will demonstrate competence in GIS techniques, geospatial data analysis, and project management at a professional level. This course may substitute for GEOG 3150 - GIS Project Management. Prerequisites: Completion of a minimum of two GISc courses.
MATH 3311 Linear Programming (4 Credits)
Linear optimization models, simplex algorithm, sensitivity analysis and duality, network models, dynamic programming, applications to physical, social and management sciences. Prerequisite: MATH 2060.
MATH 4701 Combinatorial Algorithms (4 Credits)
Basic enumeration techniques; representations of combinatorial objects; algorithms for searching, sorting, generating combinatorial objects, graph algorithms. Prerequisites: MATH 3701 or MATH 3710.
PHYS 3611 Electromagnetism I (4 Credits)
First of a two-quarter sequence. Vector algebra; differential vector calculus (gradient, divergence and curl); integral vector calculus (gradient, divergence and Stokes’ Theorems); line, surface and volume integrals; Electrostatics: the electric field, electric potential, work and energy in electrostatics; method of images, boundary value problems and solutions to Laplace’s equation in Cartesian, spherical and cylindrical coordinates; multipole expansion of the electric potential; electric fields in matter: polarization; the electric displacement vector; boundary conditions, linear dielectrics. Magnetostatics: magnetic fields and forces. Prerequisites: PHYS 1113, PHYS 1213, or PHYS 1214 and MATH 2070.
PHYS 4860 Numerical and Computational Methods in Physics (4 Credits)
The main goal of this course is to gain a better understanding of physical problems by solving them numerically; in the process, students learn about several numerical methods and computational techniques that have a very broad range of applications in many other scientific fields. Depending on the problem, students work with a software package (Mathematica), and also acquire coding experience in different programming languages. Graduate students carry out projects involving more complex simulation and numerical methods currently used in many areas of condensed matter physics, quantum chemistry and biophysics, such as Density Functional calculations, Monte Carlo and Molecular Dynamics methods.
LAWS 4143 Commercial Paper (2,3 Credits)
This course introduces students to Article 3 of the Uniform Commercial Code, Negotiable Instruments. After studying this part of the UCC at the beginning of the semester, students will engage in a simulated, complex business transaction for the remainder of the course. The simulation involves problem solving, extensive document drafting, client counseling and professionalism, among other topics. The simulation involves transactions in a business/banking context, but is not an overview of banking law.
LAWS 4376 Law and Emerging Technologies (3 Credits)
Technological innovations have created challenges for regulators and policymakers. This course focuses on recent emerging technologies and introduces law students to ways law interacts with technology. Students are encouraged to think creatively to address the current challenges and anticipate future legal impacts. It also familiarizes students with the recent legal scholarship in this field. Topics covered in this course include autonomous vehicles, AI and facial recognition, big data, digital privacy, deep fakes, platform governance, and quantum computing.
LAWS 4423 Legal Databases Research (3 Credits)
This course introduces students to a variety of legal databases, both fee-based and free, that can be utilized for conducting effective legal research as a student and practicing lawyer. Students learn to analyze and critically evaluate whether or not a database provides accurate information and resources. Students learn to determine which legal databases are most useful for specific types of information and resource needs. Students learn to construct successful search strategies that can be employed to search a database and find the information required. This course equips students to become expert searchers in the online environment.
LAWS 5027 American Association for Justice Trial Team (3 Credits)
Sturm’s American Association for Justice Trial Team (AAJ) is one of four of the school’s advanced courtroom-simulation based “team-courses” in which students find themselves working intensely with five other students and an instructor, delving deeply into increasingly complex areas of case analysis, evidentiary interpretation and application, examination drafting and presentation, and ethical dynamics of fact patterns. Students must invited to be on one of Sturm’s National Trial Teams, after tryouts that are held every spring (April). The team-courses meet all three semesters (fall, spring and summer). Being invited onto one of the teams is a two-year commitment. Starting the first week of each semester, and continuing throughout the entire semester, the AAJ team-course meets once a week in a classroom environment, for a total of 2.5 classroom hours per week. The AAJ team-course also meets a second time each week for courtroom simulation performances and video review, for an additional four (4) hours. There is also substantial out-of-class case analysis and examination drafting required. The team competes on the national level in the fall at an “invitational” tournament, and in the spring in the AAJ tournament against other ranked law schools from around the country. Grading is based on classroom participation, written homework, and simulated courtroom presentations. Prerequisite: LAWS 4635.
LAWS 5028 ABA Trial Team (3 Credits)
Sturm's ABA/NTC team is one of four of the school's advanced courtroom-simulation based "team-courses" in which students find themselves working intensely with five other students and an instructor, delving deeply into increasingly complex areas of case analysis, evidentiary interpretation and application, examination drafting and presentation, and ethical dynamics of fact patterns. Students must be invited to be on one of Sturm's National Trial Teams, after tryouts that are held every spring (April). The team-courses meet all three semesters (fall, spring, and summer). Being invited onto one of the teams is a two-year commitment. Starting the first week of each semester and continuing throughout the entire semester, the ABA/NTC team-course meets twice a week in a classroom environment, for a total of five (5) classroom hours per week. The ABA/NTC team-course also meets a third time each week for courtroom simulation performances and video review, for an additional four (4) hours. There is also substantial out-of-class case analysis and drafting required. The team competes on the national level in the fall at an "invitational" tournament, and in the spring in the ABA/NTC tournament against other ranked law schools from around the country. Grading is based on classroom participation, written homework, and simulated courtroom presentations.
ANTH 3030 Digital Anthropology (4 Credits)
Digital Anthropology introduces students to computer technology used in anthropological research. Students study and then produce a number of digital products useful in the analysis and interpretation of museum collections, for archaeological mapping and research, and for the dissemination of anthropological knowledge online. This process covers the use of Geographic Information Systems (GIS) for spatial analysis, three-dimensional imaging programs ranging in scale from broad landscape mapping to detailed digital artifact analysis. In addition, the use of geophysical methods for imaging what is below the surface allows students to produce images of what lies below the ground in archaeological contexts.
ANTH 3130 The Archaeology of Gender (4 Credits)
This course examines the ways archaeology can contribute to the study of gender through investigations of the deep through recent past. The class will include readings on gender theory, the uses of archaeological data and specific case studies of engendered lives in the past. Cross listed with GWST 3130.
ANTH 3620 Ethnoarchaeology (4 Credits)
Ethnography has often been used as an illustrative device to animate archaeological remains, or to develop models of human behavior, regardless of the geographic and chronological distance between the ethnographic and the archaeological data. This course addresses different perspectives and theories concerning the use of ethnoarchaeology to complement archaeological information. It aims to define the role of ethnoarchaeology in the study of human past; to establish an agenda of issues to which their use is relevant; and to provide a critical overview of major approaches to the use of ethnographic analogies and historical information in archaeology.
ANTH 3660 Anthropological Theory and Context (4 Credits)
History and development of particular schools of thought, paradigms, methods and methodologies that characterize contemporary anthropology. Intellectual, artistic developments, world-wide sociopolitical and economic processes that shaped much of anthropological thinking of the times. Research methods in reconstruction of human history and qualitative ethnolographical research.
ANTH 3750 Ethnographic Methods (4 Credits)
In this course, students study the art and science of ethnographic research methods, conduct quarter-long field research projects, and write practice ethnographies. The course requires students to apply the American Anthropological Association's Code of Ethics in their research and to write Institutional Review Board applications for their projects. Course readings include texts on ethnographic methods as well as controversial and exemplary ethnographic publications for student dissection and debate.
ANTH 3790 Field Methods in Archaeology (4 Credits)
The purpose of this class is to introduce students to archaeological field methods through a combination of readings, lecture, discussion, and hands-on experience. Training begins with issues of archaeological ethics, legal mandates, and research designs. Students then transition to learning skills and methods both in the classroom and in the field. Methods you will learn will include the basics of site survey and mapping, testing, excavation, artifact recovery and field processing, and data recording in the field. Cross-listed with ANTH 1790. Prerequisite: ANTH 2310.
ANTH 4660 Anthropological Theory and Context (4 Credits)
History and development of particular schools of thought, paradigms, methods and methodologies that characterize contemporary anthropology. Intellectual, artistic developments, world-wide sociopolitical and economic processes that shaped much of anthropological thinking of the times. Research methods in reconstruction of human history and qualitative ethnolographic research.
EDPX 4270 Making Networked Art (4 Credits)
In this course networked art is understood in the broadest sense from art that natively exists on digital networks to art that critiques and engages with the concept of the network in contemporary society. This course aims to develop a critical understanding of and response to the social, cultural, aesthetic and technical contexts of network culture, building on a deep understanding of contemporary and historical networked art practices. Students will engage with network architectures and platforms developing experimental approaches to user interface and interaction, deploying a range of digital materials from data to rich multimedia content to create work that produces new understandings of the role of the network in a post digital age. Prerequisite: EDPX 4250, or permission of the instructor. Lab fee. Crosslisted with EDPX 3270.
EDPX 4310 Tangible Interactivity (4 Credits)
Explores methods and devices for human-computer interaction beyond the mouse and keyboard. Students learn to create and hack electronic input and output devices and explore multi-touch augmented reality, and other forms of sensor-based technologies. Lab fee. Prerequisite: EDPX 4010 or permission of the instructor.
EDPX 4490 Expanded Cinema (4 Credits)
This course introduces several forms of expanded cinema, such as video remixes and mashups; live cinema and audiovisual performance; VJing; sonic visualization; visual music; and ambient video. The class extends the student's multitrack video and audio mixing skills to an emphasis on both performance and generative approaches to audiovisual media. It introduces software and hardware sets including VJ tools and visual programming for generating as well as manipulating video files and real-time source streams. Lab fee. Cross listed with EDPX 3490. Prerequisite: EDPX 4010 or permission of the instructor.
EDPX 4600 3D Modeling (4 Credits)
This course serves as an introduction to 3D modeling, texturing, and lighting on the computer. Students complete a series of projects in which the processes of preparing and producing a 3D piece are explored. Various strategies and techniques for creating detailed models to be used in animation and games are examined. Additional attention is spent on virtual camera techniques as well as the use of composting in creating final pieces. Current trends in the field are address through the analysis and discussion of current and historical examples. Lab fee. Cross listed with EDPX 3600, MFJS 3600. Prerequisite: EDPX 4000 or permission of the instructor.
ENGL 4050 The Critical Imagination (2 Credits)
This graduate level course explores poetry, fiction, and criticism as different facets of the imagination. This is a large and a necessarily vaguely defined topic. But in the world of literary studies, creativity and criticism are clearly symbiotic. Reading and writing are connected activities. The poet or fiction writer is often a critic, and there are numerous treatments of interpretation in the critical canon suggesting that the act of reading and interpreting is itself an imaginative and creative act. The course explores genre signatures and possibilities, as well as provides an introduction to some of the analytics through which texts, literary and otherwise, are interpreted.
MUAC 3973 Advanced Wind Literature I (2 Credits)
This course is an overview of wind literature appropriate for junior high school, high school, college and professional programs including strategies in effective programming and creation of appropriate program notes.
MUEN 3046 Indonesian Music Ensemble (0-1 Credits)
This class provides a practical and theoretical introduction to Indonesian performance traditions from the islands of Bali and Java. Through hands-on instruction and oral transmission, students will learn a variety of gamelan (gong/chime ensemble) traditions. While learning this sophisticated cyclic music, class discussions, assigned readings, films, and guided listening will further familiarize students with the social and cultural meanings of the musics performed in class. Additionally, students will have the opportunity to learn basic hand, foot, and eye movements for Balinese and Javanese dance, as well as to study kecak, a Balinese vocal music that imitates the sound of the gamelan. The course will culminate in an end of the quarter concert.
RLGS 3505 Gender and Politics in Muslim Pop Cultures (4 Credits)
This undergraduate/graduate course introduces students to contemporary Muslim popular cultures, in the United States and around the world. It uses gender and politics as thematic lenses, taking a lived religions approach to phenomena that range from pious television programming to online efforts to spread Islamophobia.
COMP 3001 C and C++ Programming Foundations for New Graduate Students (4 Credits)
This accelerated course covers the basics of discrete mathematics including functions, relations, counting, logic, proofs etc that is necessary to attend CS graduate school. In addition, it includes an introduction to programming and algorithm analysis. Enrollment restricted to graduate students.
COMP 3003 Foundations in Computer Systems (4 Credits)
This course introduces computer systems, including instruction set architectures; memory hierarchies including registers, caching, virtual memory, paging, and segmentation; number representations; binary arithmetic and operations; assembly language instructions; and pipelining in the CPU.
COMP 3411 Web Programming II (4 Credits)
In this course you will learn how to develop a full-stack web application that is capable of serving dynamic content from a database. Furthermore, you will learn the core design concepts and principles that will enable you to develop scalable and easy to maintain webapplications - a set of skills that will serve you well in both your personal and professional projects in the future. Prerequisite: COMP 3410.
COMP 3412 Web Projects: Web Development III (4 Credits)
In this course you will learn how to develop, as a group, a full-stack web application that is capable of serving dynamic content from a database. We will use the MongoDB, ExpressJS, Angular, and Node.js (MEAN) software stack to work on a real-life problem presented to us by an external product owner. In the class we will use the Scrum framework for Agile development to work, as a software team, through several sprints of development. You will be peer reviewing each other throughout the course, and the product owner will also be reviewing your product through end-of-sprint demos as features are completed. The goal for this class is for it to be a fun, collaborative, and educational environment that demonstrates what it is like to work as a real software team. Prerequisite: COMP 3411.
COMP 3681 Networking for Games (4 Credits)
Implementing the networking code for multiplayer games is a complex task that requires an understanding of performance, security, game design, and advanced programming concepts. In this course, students are introduced to the networking stack and how this is connected to the Internet, learn how to write protocols for games, and implement several large games using a game engine that demonstrate the kind of networking and protocols required by different genres of games. In addition, tools are introduced that help understand and debug networking code, simplify the creation of protocols, and make the development of networking code easier.
COMP 3822 Game Programming II (4 Credits)
In this course, students learn how to work with a 3D game engine and build 3D games. Topics include algorithms, mathematics for 3D game engines, scene management, animations, 3D shaders, particle systems, physics for games, UIs, terrain systems, and working with higher-level scripting languages on top of the low-level implementation language. Prerequisites: COMP 3821. Suggested corequisite or prerequisite: COMP 3801.
COMP 4355 Advanced System Programming (4 Credits)
This course covers programming in a UNIX environment, including use of common command line utilities, scripting, source control via Git, and integration of POSIX system calls into C/C++ code. These features will be leveraged to solve practical problems cleanly and efficiently. More emphasis will be placed on using these features than on how those features work. Prerequisites: COMP 3001, 3002, 3003, and 3004.
COMP 4384 Secure Software Engineering (4 Credits)
This course is concerned with systematic approaches for the design and implementation of secure software. While topics such as cryptography, networking, network protocols and large scale software development are touched upon, this is not a course on those topics. Instead, this course is on identification of potential threats and vulnerabilities early in the design cycle. The emphasis in this course is on methodologies and paradigms for identifying and avoiding security vulnerabilities, formally establishing the absence of vulnerabilities, and ways to avoid security holes in new software. There are programming assignments designed to make students practice and experience secure software design and development. Prerequisites: COMP 3006, COMP 3361, COMP 3356.
COMP 4433 Data Visualization (4 Credits)
This course explores visualization techniques and theory. The course covers how to use visualization tools to effectively present data as part of quantitative statements within a publication/report and as an interactive system. Both design principles (color, layout, scale, and psychology of vision) as well as technical visualization tools/languages will be covered. Prerequisites: COMP 3006.
COMP 4722 Network Security (4 Credits)
Network Security covers tools and techniques employed to protect data during transmission. It spans a broad range of topics including authentication systems, cryptography, key distribution, firewalls, secure protocols and standards, and overlaps with system security concepts as well. This course will provide an introduction to these topics, and supplement them with hands-on experience. In addition, students will perform an extensive analysis, or development of a security related product independently. Prerequisites: COMP 4721 or COMP 3001, 3002, 3003, and 3004.
COMP 4723 Ethical Hacking (4 Credits)
Ethical hacking is the process of probing computer systems for vulnerabilities and exposing their presence through proof-of-concept attacks. The results of such probes are then utilized in making the system more secure. This course will cover the basics of vulnerability research, foot printing targets, discovering systems and configurations on a network, sniffing protocols, firewall hacking, password attacks, privilege escalation, rootkits, social engineering attacks, web attacks, and wireless attacks, among others. Prerequisites: COMP 3361, or COMP 3001, 3002, 3003, and 3004.
COMP 4732 Human-Centered Data Security and Privacy (4 Credits)
With an increasing digital presence, it is critical to understand users' needs and requirements in using technological equipment to secure interactions and adhere to privacy perceptions. Thus, it is essential to analyze the cognitive, social, organizational, commercial, and cultural factors in mind. This course will provide a socio-technical approach for analyzing critical user interaction with devices encountered in everyday life, including web, mobiles, and wearables. This course will help students develop an understanding of technological interactions from the perspectives of multiple stakeholders such as users, developers, system administrators, and others and build tools to protect user data.
COMP 4799 Capstone Project in Cybersecurity (1-8 Credits)
The purpose of the cybersecurity capstone project is to provide an integrative experience that ties together the learning outcomes from academic coursework undertakings and industry skills necessary to be productive in delivering an end product. Students will engage in one of many options available, such as involvement in a research project, a case study, a product development project, or an extensive survey paper. Capstone projects are presented at the end of the quarter in front of a representative group. Prerequisites: COMP 3001, 3002, 3003, and 3004.
ENCE 3631 Machine Learning (4 Credits)
Design, construction and testing of microprocessor systems. Hardware limitations of the single-chip system. Includes micro-controllers, programming for small systems, interfacing, communications, validating hardware and software, microprogramming of controller chips, design methods and testing of embedded systems.
ENCE 4110 Modern Digital Systems Design (4 Credits)
This course covers advanced concepts in Hardware Description and Language (HDl) modeling and Synthesis. It covers topics including but not limited to digital system design, simulation, and synthesis using Verilog HDL and VHDL. The course also covers RTL design, behavioral description, system Verilog, and timing analysis using CAD tools.
ENCE 4620 Advanced Computer Vision (4 Credits)
Various topics in computer engineering as announced. May be taken more than once. Cross-listed with ENCE 3321, ENCE 3620.
ENEE 4630 Optical Networking (4 Credits)
This class does not meet. All graduate (MS and PhD) ECE students will enroll in this class during their last quarter. All required assessment materials will be uploaded online in Canvas Assignments to meet the course requirements. Students will receive Canvas course announcements and or emails from the instructor notifying the students of what are required to be uploaded. The purpose is to collect data for the assessment and continuous improvement of the graduate programs.
ENSY 4090 Project Management in Relation to Systems Engineering (3 Credits)
This class does not meet. All MS in Systems Engineering (MSSY) graduate students will enroll in this class during their last quarter. The purpose is to collect data for the assessment and continuous improvement of the graduate programs. All required assessment materials will be uploaded online in Canvas Assignments to meet the course requirements. Students will receive Canvas course announcements and or emails from the instructor notifying the students of what are required to be uploaded.
ENBI 4200 Medical Device Development (4 Credits)
Working in a fast-paced competitive biomedical R&D firm is a dramatic change of pace from most college classes. This course will create a realistic industry environment where students take on the role of development engineers to design and manufacture real-world medical devices. This course is intended to provide a working knowledge of the design and development process specifically for medical device applications.
ENME 3320 Computer Aided Design and Analysis (4 Credits)
Introduction to the use of computer aided design and analysis with applications to solid and fluid mechanics, heat transfer and vibrations; projects in one or more of the above areas. Emphasis on how to use the software to analyze engineering systems. Prerequisites ENME 2541 and ENME 2651.
Building on the principles and applications of computational methods in fluid flow and topics chosen from heat transfer, mass transfer and two phase flow. Specifically, Monte Carlo and volume of fluid techniques are discussed at length. Additionally, students learn how to set up automated design optimization using the latest software packages. Time permitting, students also are introduced to fluid-solid interaction modeling. Prerequisite: ENME 3651.
ENGR 3525 Power Electronics and Renewable Energy Laboratory (1 Credit)
In this course the fundamentals of switching converters and power electronics in a real laboratory set-up are covered. The course incorporates hardware design, analysis, and simulation of various switching converters as a power processing element for different energy sources. The energy sources are power utility, batteries, and solar panels. Prerequisite: ENGR 3520.
ENGR 3540 Electric Power Systems (4 Credits)
This course covers methods of calculation of a comprehensive idea on the various aspects of power system problems and algorithms for solving these problems. Prerequisite: ENGR 3530.
ENGR 3731 Robotics Lab (1 Credit)
Laboratory that complements the analysis, design, modeling and application of robotic manipulators. Implementation of the mathematical structures required to support robot operation. Topics include forward kinematics, inverse kinematics, motion kinematics, trajectory control and planning and kinetics. Applications include programming and task planning of a manufacturing robot manipulator. Corequisite: ENGR 3730 or permission of instructor.
ENGR 4100 Instrumentation and Data Acquisition (4 Credits)
This course examines different instrumentation techniques and describes how different measurement instruments work. Measurement devices include length, speed, acceleration, force, torque, pressure, sound, flow, temperature, and advanced systems. This course also examines the acquisition, processing, transmission and manipulation of data. Final project or paper. Cross listed with ENGR 3100. Prerequisites: PHYS 1213 OR PHYS 1214.
ENGR 4723 Digital Control (4 Credits)
The course focuses on modeling, analysis, and design of digital control systems. Topics include: z-Transform and difference equations; sampling and aliasing; Zero-Order Hold (ZOH); A/D and D/A conversions; pulse transfer function representation; time and frequency domain representations; input/output analysis; analysis of sample data systems; stability; design of discrete-time controllers; introduction to state-space representation. Cross listed with ENGR 3723. Prerequisites: ENGR 3721 and ENGR 3722.
ENGR 4730 Introduction to Robotics (4 Credits)
Introduction to the analysis, design, modeling and application of robotic manipulators. Review of the mathematical preliminaries required to support robot theory. Topics include forward kinematics, inverse kinematics, motion kinematics, trajectory control and planning, and kinetics. Applications include programming and task planning of a manufacturing robot manipulator. Cross listed with ENGR 3730. Prerequisites: ENME 2520 and MATH 2060 or MATH 2200 or instructor approval.
ENGR 4750 Networked Control Systems (4 Credits)
Fundamental tools and recent advances in networked control. Topics include the control of multi-agent networks found in multi-vehicle coordination, control of sensor networks, unmanned vehicles, and energy systems. Network models, distributed control and estimation, distributed control under limited communications and sensing, formation control, coverage control in mobile sensor networks. Prerequisites: linear algebra, linear control systems, differential equations, familiarity with MATLAB, or permission of instructor.
INFO 4200 Business Analytics Capstone Planning (2 Credits)
This course prepares the student for the Capstone course by identifying a faculty advisor, company, data, and a business issue to be addressed in the Capstone course in the final quarter. (Must be taken two quarters prior to INFO4400, with the exception of off-cycle students, who will take it the quarter prior to INFO4400.) This course may be taken by MSBA students only.
INFO 4400 Business Analytics Capstone (4 Credits)
This course gives students an opportunity to apply the knowledge and skills learned in this program to a real-world problem submitted by a partner business. Students take a business problem from model construction and data collection through an analysis and presentation of results to recommendations for specific business decisions. Prerequisite: INFO 4200.
INFO 4590 Optimization (4 Credits)
This course introduces students to the basic optimization modeling techniques and tools as practiced by business analysts to help their enterprises make better-informed decisions. Applications will include mix, selection, assignment, distribution, transportation, financial management, planning, scheduling, and management implementations in a variety of business settings. The course will focus on problem definitions, problem configuration, spreadsheet solutions, LP Software (LINGO) solutions, and interpreting and implementing results.
INFO 4700 Topics in Business Analytics (0-10 Credits)
Exploration of current trends and topics in business analytics. Prerequisite: INFO 4100.
BUS 4105 Masters in Digital Leadership Capstone Course (4 Credits)
What makes a good Digital Leader. It’s the ability to lead organizations using emerging technologies and possessing a data-driven mindset in efforts to digitally transform organizations and industries. This course gives the student an opportunity to apply the knowledge and skills learned in this program to a real-world problem that affect them and their organizations. Students take a business problem from problem definition to digital transformation. This isn’t a course where students write about a problem, but rather work with mentor to create value for themselves and their organization. Prerequisites: All other Digital Leadership courses.
BUS 4132 Mobile Technology (1 Credit)
You’ve probably heard about the 5th Generation of mobile technology (5G), but have you heard about the 6th (6G)? Consumers are increasingly becoming more mobile and are demanding more data be delivered to them more quickly. It seems now that the value of mobile technology is centered around increasing demand. This puts an enormous strain on organizations as they begin to digitally transform their organizations. Conversely, for the informed digital leader, this pattern of rapid mobile technology is an opportunity to provide newer and better value for their stakeholders. This class will explore these challenges and opportunities and will provide learners with an insight into the emerging ethical challenges that are related to the use of mobile technology.
BUS 4136 Robotic Process Automation (1 Credit)
Robotic Process Automation (RPA) is an emerging technology that is changing the way businesses process data. RPA allows many business processes to be automated and remove the human from performing repetitive tasks. This course will teach the basics of the technology using one of the most popular RPA software programs.
BUS 4142 Business Model Innovation (2 Credits)
If companies don’t innovate, they evaporate. This is especially true as an organization begins the digital transformation process. In this class, students will be taught how to evaluate an organization’s business model to facilitate its transition to the digital realm. The idea is to transition a business model into something that creates value by way of leveraging emerging technologies, analytics, and digital leadership for all stakeholders.
BUS 4143 Digital Ethics & Privacy (2 Credits)
Do individuals have a right to digital privacy and what are the ethical ramifications that support our virtual existence? In this class, you will explore the idea of digital privacy and how businesses are balancing the need to make a profit while simultaneously safeguarding their stakeholder’s data. In addition to digital privacy, students will develop the ability to evaluate emerging technologies through varying ethical lenses and begin to explore the future directions of digital ethics.
BUS 4145 Cloud/Edge Computing (2 Credits)
Is it more accurate to say the digital cloud is above us, or all around us? In this class, students will learn to distinguish between cloud and edge computing and will be able to articulate the value of each to their organization. Students will also become familiar with the major cloud providers (e.g., Google, AWS, and Azure). Equipped with knowledge students will be able to create a cloud transformation plan that highlights their organization’s cloud journey and transformational process to the cloud.
BUS 4147 The Foundations of Digital Transformation (1 Credit)
What does it mean for a business to digitally transform? Even as more businesses say they are “digitally transforming”, it is still largely unclear what this process means, and perhaps more importantly, how it can be successfully achieved. This course is designed to provide students with clarity around these topics by first examining the foundations of digital transformation (emerging technology, leadership, and data) and analyzing their impact on the business. After developing this foundation, students will then work to diagnose a firm’s ability to transform by evaluating its digital capabilities to produce stakeholder value. Once students have gained insights into the context and capabilities of digital transformation, they will explore the potential ethical challenges and issues raised by moving their organization to the digital realm.
BUS 6302 Seminar in Verbal and Non-Verbal Research (4 Credits)
This course is designed to provide you with knowledge on how to leverage verbal and nonverbal behavior to identify psychological states and traits, to predict social evaluations and organizational outcomes. Together we will review theory, methods, and findings pertaining to verbal and nonverbal behavior in the psychological literature. We will learn how to develop research questions and hypotheses, design research to test those predictions, develop behavioral coding schemes, and identify appropriate statistical analyses. We will also discuss the strengths and weaknesses of extant research to determine what can and cannot be concluded from the results. Overall, this course will provide you will techniques to quantify human behavior, identify ways to leverage these techniques to answer novel questions of organizational importance, and to appreciate the limits of behavioral analysis.
BUS 6503 Applied Research Practicum IV (4 Credits)
Students, along with oversight and assistance from their respective ARP professor, will analyze data consistent with his/her research proposal (ARP II) and analysis strategy (ARP III). The students will then complete an entire research paper that is ready for presentation and/or publication at appropriate outlets.
FIN 4835 Executive Education - Finance for Non-Financial Managers (2 Credits)
This Executive Education workshop introduces you to the essential finance skills any business professional needs to know. You will learn the language, tools and techniques to become a more intelligent user of financial reports. Through hands-on learning exercises, you will learn how to simplify, understand and apply data from financial reports and budgets. You will gain the confidence to ask better questions and make more informed financial decisions. *This short-form workshop does not follow the traditional quarter schedule. Please check daniels.du.edu/executive-education for class dates and formats.
MGMT 4405 Strategic Execution and Summit Team Competition and Assessment (3 Credits)
Strategic Execution is a Challenge Driven Educational (CDE) course that builds off several previous MS Management courses. Students will leverage the contents from accounting, finance, management, marketing, strategy, and business analytics to engage with corporate partners to examine real-world problems. This course provides you with the opportunity to apply what you have learned so far in the MSM program with a live client. You will work on a project focused on business and management. Scoping the project will be a key learning outcome.
MGMT 4501 Springboard Tools I (1 Credit)
PowerPoint, Microsoft Word, Adobe, Excel, Outlook… we could hardly imagine doing business in today’s world without them. CRM software will soon be regarded in the same essential way. As technology fundamentally shifts the focus of business to a completely customer-centered environment, the pervasiveness of CRM tools will only grow. Though many of these tools exist, Salesforce is one of, if not the most, prevalent CRM tools available in today’s market. Used by companies of all sizes and industries, Salesforce provides a suite of products that allow organizations to place their customers at the heart of their businesses, leveraging customer data to gain valuable insights, and provide the customer with a wholistic and seamless experience and interaction with the company’s brand. Salesforce’s CRM software provides products for the full spectrum of customer interactions, from sales to marketing, commerce to customer service. There are several paths to developing Salesforce proficiency, ranging from a business (end) user of the product, to an administrator (someone who customizes the tool to meet business requirements), to a consultant (someone who implements Salesforce rollouts at organizations), to a marketer (an expert in Salesforce’s marketing tools), and beyond.
MGMT 4502 Springboard Tools II (1 Credit)
Your journey to securing a career upon graduation starts with the first day of classes and continues throughout your program. The Springboard Tools courses are designed to prepare students to be career-ready upon graduation. Students will learn various tools used in business today, such as Salesforce, Python, and more as determined by the business community. In addition to understanding the tool and its application, students will be required to incorporate problem-solving techniques when using a particular tool.
MGMT 4560 Leadership of the Future (4 Credits)
In nearly every aspect of life - science, business, pop culture, environment, technology, global politics - we are inundated with data about how much and how fast the world is changing. How will these major shifts impact what we think of as leadership, and how can one develop to be prepared to lead in a fast-moving, volatile, and complex world? Leadership of the Future is a course that takes a deep look at how we’ve thought about what “leadership” is in the past from a business perspective, and considers what the future will require of leaders as they seek to effectively lead and make a difference in a complex world. The course is founded upon an interdisciplinary approach, drawing from a variety of disciplines including psychology, administrative science, literature, medicine, and philosophy. The course will center around behavioral analysis and active reflective practice: together we will think deeply about leadership as a behavior within a particular context, and as a practice to cultivate. Students will articulate a set of leadership development goals for themselves and engage experientially in service of self-observation, personal growth, and learning. Cross-listed with MGMT 3560.
MGMT 4650 Introduction to Management Consulting (4 Credits)
This course is designed to provide a broad overview of the management consulting profession, including its industry and competitive dynamics, major practice areas, approaches to implementation, management of consulting firms and the future of consulting. In addition, emphasis is given to the practice of consulting through the development of certain high impact skills in evaluation, proposal writing, data gathering and client presentations. The course is relevant to those who: 1) are specifically interested in consulting careers, 2) have job interests that involve staff positions in corporations, 3) want to become line managers who might one day use consultants, 4) wish to develop general consulting skills and familiarity with the consulting industry. The learning process in class will consist of lectures, cases, readings, exercises and guest speakers. This wide variety of learning methods is intended to convey both the necessary knowledge and practical skills necessary for building a sound foundation for becoming a professional consultant. It is essential that everyone comes well-prepared to class, as the learning process depends heavily upon participation.
MKTG 4530 Marketing Research (4 Credits)
Understanding consumers requires careful observation and thoughtful questions. Marketing research represents a methodology for getting the answers needed to be successful in business. This course introduces students to a broad array of marketing research tools, including focus groups, ethnographic studies, survey research, and experiments. Students will learn how and when to apply these tools, as well as how to interpret the results to make sound marketing decisions. Highly recommended students take statistics prior to taking this course. Prerequisite: MKTG 4000, MKTG 4100, or instructor permission.
MKTG 4570 Digital Strategies (4 Credits)
We’re 20 years into the digital marketing revolution and the ecosystem continues to evolve. From the birth of the Internet and email to the recent addition of messaging apps and the Internet of Things: It’s a fantastic time to be a marketer. In this class, we will take what you learned in consumer behavior and extend it in the social/mobile/search realm. We’ll utilize lessons learned from cognitive neuroscience combined with qualitative/quantitative data to create one-to-one marketing experiences for B2B/B2C consumers. Prerequisites: MKTG 4510 or instructor permission.
MKTG 4580 Insights to Innovation (4 Credits)
Consumer insights are a driving force of change for organizations and markets. It is becoming increasingly clear that the development of novel offerings requires the contributions of multiple stakeholders, including customers. This course explores the collaborative processes that drives value creation and innovation. Students will learn how consumer insights can enable the development and enhancement of compelling value propositions. They will also utilize a design-thinking approach and work with different types of data sources in developing innovative solutions and designing consumption experiences. Prerequisites: MKTG 4100 or instructor permission.
MKTG 4810 Integrated Marketing Communication (4 Credits)
Integrated Marketing Communication is a critical component of marketing strategy and is vital to any business’s success. Organizational, technological, and societal trends of the past few years have disrupted traditional marketing communications by necessitating digital delivery in addition to traditional strategies. It’s essential to integrate all marketing communication activities into one master plan. This course is based upon the notion that marketing communications include much more than just advertising. The course provides students with a foundation in the development and execution of communications strategies for any organization (large, small, public, or private). We’ll bring clarity to the current ecosystem of digital tools and promotional strategies through data-driven decision-making. Prerequisites: MKTG 4100 & MKTG 4510 or instructor permission.
This class is focused on documenting/sharing lessons learned from the SXSWi conference in Austin Texas, the premier innovation conference in the US. The course is divided into two distinct halves. First, we will research the SXSWi sessions around subject matter and speaker backgound as well as planning the final deliverabile that summaizes the entire SXSWi event. The second half includes participation in the conference to learn the most up-to-date digital marketing techniaques in social, mobile, data and usability.
ACTG 4130 RPA in the Business and Accounting Environment (4 Credits)
Robotic Process Automation (RPA) is an emerging technology that is changing the way businesses process data. RPA allows many business processes to be automated and remove the human from performing repetitive tasks. This course will teach the basics of the technology using one of the most popular RPA software programs, UiPath. Students will learn the theory, design an application of RPA through small projects.
ACTG 4575 Accounting Information System Risk, Control and Audit (4 Credits)
An auditor cannot just “audit the numbers” without strong consideration to the IT systems that generate those numbers. Today’s accounting professionals must possess a strong understanding of accounting information system risks and controls. Topics specifically covered in this course include IT security controls, datacenter controls, data backup and disaster recovery planning, SDLC and change control processes. Students perform hands on simulated audit exercises and case studies to truly experience the role of an IT auditor. Prerequisites: ACTG 3551 or ACTG 4551 or test score AC51=1.
CMGT 4120 Construction Planning & Scheduling (4 Credits)
Understanding and applying scheduling and control to construction projects is essential to successful construction management. Project scheduling emphasizes network-based schedules, such as critical path management (CPM), network calculations, critical paths, resource scheduling, probabilistic scheduling and computer applications. Project control focuses on goals, flow of information, time and cost control, and change management. Prerequisite or Corequisite: CMGT 4420.
CMGT 4480 Const Project Management (4 Credits)
Principles and techniques of construction project management, use of systems analysis, internal and external procedures, planning, programming, budgeting and staffing, controlling major projects, emphasis on construction scheduling techniques with case application.
CMGT 4560 Relational Contracting and Risk Mitigation (4 Credits)
Relational contracting is a construction project delivery framework for multidisciplinary, integrated projects that focuses on aligned goals, high performance, innovation, mutual respect, open communication and a "no blame" culture between Client, Contractor, and Design Team. This approach to contracting, also known as Alliance Contracting, is becoming more prevalent in the United States and is often applied when using integrated project delivery systems. This course compares and contrasts transactional contracting methods with relational contracting methods and the influences on the project team and projects outcomes. Relational contracting is also considered in the context of risk mitigation and project optimization.
EVM 4350 Big Challenges, Big Solutions: The Emerging Start-Up (4 Credits)
Students in the experiential course will start a firm in which they formulate an idea, gather basic data, formulate hypotheses, and then test these hypotheses with potential market participants. Students are likely to pivot several times in this course as the experimentation process helps them shape the emerging firm.
EVM 4420 Cloud Technologies (1 Credit)
Welcome to the Cloud! What is the cloud, is it a thing, a concept, a nifty term? If you are starting a new business, thinking about starting a new business or improving the efficiencies in an existing business, you need to understand the available technologies and tools in the Cloud. Where do I host my website, how do I handle accounting, where is the email server, how do I track customers, how do I share information, what tools are available for customer support? These are just a few questions the Cloud will solve efficiently and cost effectively. The Cloud has dramatically changed the competitive landscape for startups by reducing the cost of starting a new business. The Cloud removes costly equipment, software and support expenditures; with the Cloud, you pay for what you use. This course will focus on identifying, analyzing, and implementing Cloud technologies to help run your business. Here are some of the topics we will explore and discuss: flexible costs, how and when to implement these tools, is your data safe, comparing similar services, improving collaboration.
EVM 4432 Getting to Know Your Customer (1 Credit)
Developing lasting relationships with customers requires time and energy up front. You need to get to know who your customers are and what they value before they will develop lasting relationships with your brand.
This course on Getting to Know Your Customer will introduce students to tools and data sources that can help with segmenting and targeting and developing personas that represent different customer groups.
EVM 4443 The Marketing Mix: Converting Prospects Across the B2B and B2C Buyer's Journey (1 Credit)
How do people who have never heard of a product or company become loyal customers? Marketing leaders use a variety of tactics---from social media, digital advertising, content, customer service, reviews, emails, events, and more---to convert prospective customers to loyal ones. Converting prospects across the buyer's journey from awareness to consideration to purchase in a cost-effective manner is core to every B2B and B2C marketing campaign. During this Sprint we will learn the key elements of the marketing mix and the stages of the buyer's journey they apply to.
We’ll showcase common tactics and metrics used at each stage, and focus on the importance of using attribution data to improve the effectiveness of each conversion. We will also evaluate how marketing and sales leaders effectively partner across the buyer's journey, learn how the marketing mix can vary across B2B and B2C organizations, and showcase organizations that have developed highly effective marketing mixes.
This Sprint has asynchronous work that is available 2-weeks prior to the in-person class. The asynchronous work, up to 40% of the total work for the class, is required to be completed prior to the in-person class. There is a post class project that is due two weeks after the in-person class.
ITEC 4310 Electronic Commerce (4 Credits)
This course is an overview of electronic commerce (EC) trends and techniques including the underlying technical infrastructure, traditional ED techniques such as electronic data interchange (EDI) and commerce at light speed (CALS), Internet use for EC, business models for business-to- consumer EC, marketing on the Internet, payment and fulfillment mechanisms, security and regulatory issues, and global implications. Uses lectures, cases, outside speakers from industry and field trips.
ITEC 4477 Database-Driven Websites (4 Credits)
Using state of the art technologies, this course focuses on the development of dynamic web pages. Technologies include PEARL, ASP, ColdFusion, SQL, Access, and Oracle. Cross listed with ITEC 3477. Prerequisite: ITEC 4475 or current enrollment.
MBA 4446 Advanced Sustainability (4 Credits)
This course provides an overview of current corporate sustainability approaches and the strategies and tools that help them be effective. As businesses and corporations seek to create social and environmental impact along with shareholder value, they are developing strategic approaches to sustainability that can be measured, managed, and reported to investors, employees, and other stakeholders. Getting beyond sustainability basics to create real value and impact can build competitive advantage, attract capital investment, recruit talent and customers, and reduce negative externalities like poor water and air quality, greenhouse gas emissions, and social harm.
How can organizations move beyond “check the box” sustainability to real impact and measurable value? They need to think strategically, integrate sustainable approaches into operations, create reporting structures for good data and accountability, and create a culture around steady sustainability improvement. While corporations may approach sustainability with different moral or economic motivations and rest along a spectrum of intention and commitment, the external landscape is shifting. In the business world, customers, employees, investors, and partner firms are placing more emphasis on transparency to guide their decision-making, and businesses need to communicate their sustainability efforts effectively to diverse stakeholders. In response, both large corporations and smaller ventures are designing strategic approaches for sustainable operations (including sourcing and supply chains), cost-effective measurement, and clear reporting and sharing with their many stakeholders.
Students will gain an overview of the theory, practice, and challenges of corporate sustainability today, learn strategies and tools for designing effective approaches, and how corporations are measuring and managing sustainability outcomes to align with ESG and global development goals. The course will deepen students’ understanding of corporate sustainability strategy, the strengths and limitations of different frameworks to measuring outcomes, and highlight diverse career paths in sustainability and corporate social responsibility.
STAT 3920 Strategic Management of Operations (4 Credits)
The operations function is the unit of the organization that produces the products and/or delivers the service for which the company earns revenue. It is the largest unit of the organization with which all other units interact. Therefore, efficient management of this function is a critical success factor for any company. This course focuses on an organization's management (planning, organizing, staffing, directing, and controlling) when converting inputs into products and services. Companies today must remain competitive in the global marketplace, and careful consideration of various options regarding cost containment and use of technology are required. This course will explore how operations managers meet these challenges in the manufacturing and services firms in response to changes in economic conditions. Students will be exposed to a number of quantitative tools as well as becoming familiar with new systems and methods in the operations management field. When appropriate, optimization software such as Microsoft Solver will be utilized to conduct analysis. Prerequisite: STAT 3900.
STAT 4680 Sampling Theory & Application (4 Credits)
Simple and stratified random sampling; multistage, cluster, and sequential sampling; optimum allocation and economic efficiency; ratio estimation methods; design of sample studies of various human and physical populations; financial auditing by probability sampling. Prerequisite: MBA 4111, MBA 4112, or permission of instructor.
STAT 4870 Advanced Statistics (4 Credits)
Discrete and continuous probability distributions, sampling distributions, estimation methods, moment generating functions, analysis of variance, test of reliability, and significance by parametric and non-parametric methods. Prerequisites: MBA 4111, MBA 4112, or permission of instructor.
This seminar focuses upon the range of research topics and methods in religious and theological studies by examining dissertations and dissertation proposals related to the Joint Ph.D. Program at Iliff and the University of Denver. Bibliographic and research methods and matters of style and format receives particular emphasis. Students present their own dissertation proposals for discussion.
CPSY 4320 Cognitive Assessment (3 Credits)
Students learn to administer, score, and interpret the WAIS. There is some exposure to other intelligence tests as well. Students understand diagnostic validity (Bayes' Theorem), how to identify interpretive material, and how to think ideographically about nomothetic data. Through discussions of legal cases, students learn numerous forensic issues to which cognitive assessment is applicable, including for example testamentary capacity, competence to waive Miranda rights, and ability to enter a contract.
CPSY 4323 Issues in Measurement & Cognitive Assessment (3 Credits)
In this course, students will apply their critical thinking and analytical skills to psychological and forensic assessment, with an emphasis on validity, reliability and issues of standardization. Lectures will cover the historical bases of assessment and measure design and will also highlight contemporary approaches to testing. The course will provide exposure to recent social criticisms and ethical concerns surrounding psychological testing. Students will also learn to administer, score, and interpret the WAIS. Students will have exposure to other assessment measures (WISC, WIAT, WRAT) and approaches to diagnosis cognitive and learning disabilities. Students will understand diagnostic validity, how to identify interpretive material, and how to think ideographically about nomothetic data. Through discussions of legal cases, students learn numerous forensic issues to which cognitive assessment is applicable, including competence to waive Miranda rights, and ability to enter a contract. Corequisite: CPSY 4323.
CPSY 5050 Advanced Statistics (3 Credits)
This course is designed to increase students understanding of advanced analytical techniques in statistics, particularly as they pertain to psychology. We will take an applied approach, i.e., the course material will emphasize the feasibility, application, and utilization of these analyses rather than the theories upon which they are based.
CPSY 5073 Qualitative Research Methods (2 Credits)
Qualitative research involves obtaining in-depth information about the behaviors and beliefs of people in naturally occurring social settings. This course introduces students to the philosophical underpinnings, history, and key elements of five qualitative approaches: narrative research, phenomenology, grounded theory, ethnography, and case study. We compare theoretical frameworks and methodologies, experience the use of data, and discuss writing strategies. In addition, we read articles that are exemplars or each approach.
CPSY 5130 Issues in Measurement (3 Credits)
Validity, reliability and standardization issues in psychological testing; statistical properties of commonly used tests.
This course covers the philosophical foundations, assumptions, and principles relevant to behavioral assessment and case formulation tactics. Emphasis is given to the philosophy of science called radical behaviorism and its behavior-analytic functional-contextualistic traditions. This course specifically targets an empirical data-driven approach to idiographic assessment for purposes of developing conceptual analyses from the contextual- functional analytic perspective. Prerequisites: CPSY 5420, CPSY 5422.
This class is for students who wish to learn the skills necessary to conduct comprehensive psychological assessments in a competent, ethical, antiracist and culturally informed manner. This course will focus on learning how to integrate multiple personality measures into a cohesive understanding of one’s personality. Students will be taught how to write a traditional integrated personality report and several weeks will be spent on Collaborative/Therapeutic Assessment (C/TA). Students will be exposed to several personality tests including the Minnesota Multiphasic Personality Inventory – 3 (MMPI-3), Rorschach Performance Assessment System (R-PAS), Early Memories Procedure, Wartegg Drawing Completion Test (Crisi Wartegg System), Thematic Apperception Test (TAT), Trauma Symptom Inventory – 2 (TSI-2) and the Thurston Cradock Test of Shame (TCTS), among others. Prerequisites: CPSY 5130, CPSY 5680, CPSY 5690, & CPSY 5705.
CPSY 5826 Latinx & Underserved Populations Advanced Practicum I-Aiming to Reduce Mental Health Disparities (3 Credits)
According to the American Psychological Association, only 5.5 percent of psychologists who identify as Latinx or another race/ethnicity report that they are able to use Spanish to provide clinical services (Smith, 2018). Given the increasingly large percentage of Latinx in the U.S. and of individuals who speak another language other than English, the probability that present and future psychologists and mental health providers will provide services to Latinx and other underserved populations, is extremely high. Future and present providers will need training on how to provide culturally and linguistically appropriate services whether they are bilingual or monolingual English speakers. Moreover, there are mental health and health disparities that are shared among many underserved populations that can be partly addressed through developing a work force armed with knowledge and expertise in reducing cultural, linguistic and regional barriers to mental health. This course is designed to provide training to graduate students on how to provide culturally and linguistically appropriate services to Latinx and other underserved populations including communities of color, speakers of other languages than English, Immigrant and Refugee populations and rural communities. While there is growing attention and interest in health and behavioral health to address underserved populations, students in the health profession also voice an interest in receiving mentorship and networking to find jobs in these areas(Edwards-Johnson, Phillips, & Wendling, 2020). This class will also aim to provide information about the job market in U.S. and Denver that provide services to Latinx and underserved populations and will host presenters from the community who currently work in Denver in these settings.
According to the American Psychological Association, only 5.5 percent of psychologists who identify as Latinx or another race/ethnicity report that they are able to use Spanish to provide clinical services (Smith, 2018). Given the increasingly large percentage of Latinx in the U.S. and of individuals who speak another language other than English, the probability that present and future psychologists and mental health providers will provide services to Latinx and other underserved populations, is extremely high. Future and present providers will need training on how to provide culturally and linguistically appropriate service. This is especially true for bilingual providers and trainees who because of their bilingual skills at times are placed in mental health settings to provide bilingual services, without the proper training and/or supervision or administrative support, which can lead to poor quality service delivery and/or burn out for the provider/trainee. This in turn compounds the already existing problem of limited access to mental health services for Latinx and underserved populations. In addition, there are mental health and health disparities that are shared among many underserved populations that can be partly addressed through developing a work force armed with knowledge and expertise in reducing cultural, linguistic and regional barriers to mental health. This course, largely in Spanish, is designed to sharpen student’s clinical skills by examining current cases and analyzing appropriate intervention and assessment techniques as a class. Students will formally present cases from their current caseload, in traditional case presentation format. We will base our following discussions in Latinx psychological theory and orientations. Case discussions will be led by students and will be positive, constructive, and ethical. It will be important for students to remain open to feedback, new approaches, constructive criticism, and exploring their strengths and weaknesses as early clinicians among their peers and professor. This training is also meant to bring awareness to the students on mental health and health disparities that exists among underserved populations including racial, economic, regional (i.e. rural), cultural and language barriers. This course is designed to assist the student in the management of their Latinx caseload and seek advisement from the class on professional issues encountered as a psychologist and mental health providers in service of the Latinx population and underserved populations, in order to maintain both an ethical and realistic professional perspective. The class will be made up of class reading discussions, student presentations, community provider presentations, class activities, and class discussions. Grades will consist of professional-level class participation, two case presentations, and class activities/exercises. Lastly, while there is growing attention and interest in health and behavioral health to address underserved populations, students in the health profession also voice an interest in receiving mentorship and networking to find jobs in these areas (Edwards-Johnson, Phillips, & Wendling, 2020) This class will also aim to provide information about the job market in U.S. and Denver that provide services to Latinx and underserved populations and will host presenters from the community who currently work in Denver in these settings.
CPSY 5832 Caregiver-Child Assessment in IECMH: The Process of Assessmnt, Diagnosis, Report Writing, & Feedback (2 Credits)
Intensive training will be offered in the process of assessing a caregiver and child relationship in a manner designed to inform dyadic treatment planning. All students will be trained in conducting a multi-modal, relationship-based assessment with a caregiver and child under the age of six. Assessment tools used will include the Infant Toddler Mental Status Exam (ITMSE), the Crowell Procedure and the Working Model of the Child Interview (WMCI). Students will also be introduced to the Interpersonal Inventory and paper and pencil means of assessing the individuals and their relationship. Students will be introduced to diagnosis in IECMH using the Diagnostic & Statistical Manual of Mental Disorders – fifth edition (DSM-V) and the Diagnostic Classification of Mental Health & Developmental Disorders of Infancy and Early Childhood (DC:0-5) classification systems, as well as crossover considerations between the two systems. Students will conduct a thorough and multi-modal assessment of a caregiver-child relationship and will integrate the information learned into a professional report. Students will practice treatment planning as well as providing feedback to the dyad.
INTS 4056 Information Management in Humanitarian Crises (4 Credits)
Accurate, reliable and timely data collection, processing, analysis and dissemination (four steps in information management) are critical for the effective implementation of both development and humanitarian programs. In humanitarian responses, there are numerous challenges to managing information in what may be a rapidly evolving situation. This course introduces students to the theory of information management and its application in the humanitarian context.
INTS 4105 Campaigns and Foreign Policy (4 Credits)
This course will examine the principles of political campaign management and their application to international political campaigns, foreign policy initiatives and international affairs. Students will be introduced to the tools of political campaign management: message development, survey research, audience targeting, and paid and earned communications. Case studies will focus on elements of both US and other nations’ foreign policy. Examples of foreign policy playing a significant role in campaigns in the UK, and Denmark will be highlighted. In addition, there will be a focus on human rights and issue campaign. Classes will be comprised of lectures, discussion and some simulation exercises. Outside specialists will be invited to share their experience and expertise in person or via teleconference. Readings include contemporary journals, periodicals, newspaper reports and excerpts from major studies of campaign and organizational management. Movies and the Internet will be an integral aspect of the class.
INTS 4333 International Project Design and Monitoring (4 Credits)
It can be beneficial for graduate students planning careers in multilateral and bilateral development agencies, non-profit organizations, private-sector companies, and professional services organizations to have an understanding of how to develop a project proposal, implement it, and evaluate its results. These are useful skills for entering or reentering employment with these organizations. The Josef Korbel School of International Studies currently offers a trilogy of courses in international project cycle management—international project design and monitoring, project management, and international project evaluation. The three courses are delivered in sequence during the academic year in conformance with the project cycle, but they can be taken out of sequence without prerequisite or need to take them all. Each course uses monitoring and evaluation methods and means to connect the design, management, and evaluation of a project. Students may have been exposed some of these methods in courses covering quantitative and qualitative techniques and field research methods. Each course also shares in common the development teams and managers of those teams to produce the key deliverables at three key stages of the international project cycle. The purpose of the International Project Design and Monitoring course (formerly International Project Analysis) is to provide students with an appreciation for the myriad of considerations in designing and monitoring an international development intervention and exposure to conventional and unconventional methods and means for doing so. The international project cycle begins with identifying an intervention to address a development impediment or opportunity faced by a target group. A development intervention typically falls into a sector or thematic area, such as education and health care, and it is generally directed towards physical, human, institutional/legal capacity building, or a combination of them. Projects can be singular in scope, such as building a new primary school, or broadly scoped to mitigate causes of poverty, such as the Millennium Development Villages project, but they all should be a unique endeavor with a beginning and an end. Much of the physical development today is supported by the private sector or state sponsored organizations, with less support through traditional foreign aid unless it is a major reconstruction effort like in Afghanistan. In this course, students will learn that a project proposal should be designed in concert with the beneficiaries to be relevant, feasible, and supported by their needs, but also recognizing their absorption capacities. Such a project proposal should ideally have gone through a systematic analysis of factors that will affect its design and management of risk, including economic, financial, environmental, technical, and social factors, as well as special safeguard areas. Students will also learn about the continued need for project proposals to define the underlying theory of change, assumptions, and logical framework for linking inputs, activities, outputs, outcomes and ultimately desired impacts. Establishing a performance management plan for the project that defines, among other things, the metrics and milestones for monitoring the process is an essential component of most project proposals. However, students will learn that adherence to plans is challenging under complex development conditions.
INTS 4342 Project Management (4 Credits)
It can be beneficial for graduate students planning careers in multilateral and bilateral development agencies, non-profit organizations, private-sector companies, and professional services organizations to have an understanding of how to develop a project proposal, implement it, and evaluate its results. These are useful skills for entering or reentering employment with these organizations. The Josef Korbel School of International Studies currently offers a trilogy of courses in international project cycle management—international project design and monitoring, project management, and international project evaluation. The three courses are delivered in sequence during the academic year in conformance with the project cycle, but they can be taken out of sequence without prerequisite or need to take them all. Each course uses monitoring and evaluation methods and means to connect the design, management, and evaluation of a project. Students may have been exposed some of these methods in courses covering quantitative and qualitative techniques and field research methods. Each course also shares in common the development teams and managers of those teams to produce the key deliverables at three key stages of the international project cycle. The purpose of the Project Management course is to expose students to right- and left-brain approaches to managing the knowledge areas of project management, such as time and cost management, as well as approaches used by project managers and their teams. This course concentrates on the implementation and completion/transition phases of the international project cycle. The implementation phase commences after stakeholders approve a project proposal—translated into a project charter—from which a detailed project management plan is developed to execute the project. Project managers rely, to a large extent, on internationally recognized management approaches to move workflow smoothly among project phases, allocate project tasks effectively, efficiency track project milestones, and make adjustment for inevitable and often uncontrollable project delays and cost overruns. The completion/transition phase ends the project and transfers control from the project team to the operational team, preferably through a defined exit strategy. The course covers the knowledge and skills needed to meet the educational requirements for certification by the Project Management Institute (PMI). PMI serves practitioners and organizations by providing standards that describe leading practices, globally recognized credentials that certify project management expertise, and resources for professional development, networking and community. PMI credentials certify your knowledge and experience in project management so you can be more confident at work and more competitive in the job market. Several other organizations will be mentioned that also provide certification, but all share in common required education hours, years of experience, and passing a professional examination. Students in the course will exhibit their new knowledge and skills by joining small teams to prepare a professional project management plan for the selected development project charter and through individual examination.
INTS 4437 American Public Opinion & Foreign Policy (4 Credits)
This course examines American public opinion and its impact of foreign policy. The course begins with an investigation of what is public opinion in general and how it is collected, analyzed and used. The primary sources of American public opinion data and analyses are identified. The course proceeds to outline the controversies of American public opinion related to foreign policy decision-making using historical perspectives and the most recent challenges from the first Iraq War to the Arab Spring. Although foreign policy is often a secondary issue for the public compared to domestic issues, in recent times it has been mostly responsible for the transition from a Republican-dominated era to the Democrats’ ascendance. A series of principles that have informed practitioners and foreign policy experts concerning American opinion related to foreign policy is examined and affirmed or debunked. Also, media and its persuasive power in opinion formation are considered. At the conclusion of the course, students should be familiar with a selection of foreign policy challenges that America has confronted in the modern era, the role of public opinion in the national decision-making and the existence of guiding principles of public opinion and their exceptions.
INTS 4497 International Campaign Management (4 Credits)
This course will examine the principles of political campaign management and their application in a number of international political, public affairs and human rights campaigns. It will be an introduction to the tools of political campaign management: message development, survey research, audience targeting, paid and earned communications, fundraising and organizational structure. Case studies of campaigns in countries such as Sweden, the UK, and Australia will be used as examples of these techniques. Class will be comprised of lectures, discussion and some simulation exercises. Efforts will be made to bring outside specialists and experts to the class or by teleconference. Readings may include contemporary journals, periodicals, newspaper reports and excerpts from major studies of campaign and organizational management.
INTS 4499 Evolving Global Security Landscape (4 Credits)
This course is for Korbel in DC participants only. Change brings with it challenges—at the individual, organizational, and systemic levels. It involves behaviors and cultures with often deep-seated traditions. This course will explore the scope and magnitude of the transformational forces at work in the U.S. and to a lesser extent the global security and defense establishments. By its nature the course will be about peace and war—how the nation goes about the business of preparing, equipping, and training itself to deter and if necessary to fight traditional wars and the new kinds of challenges that might lead to armed conflict as well as shaping the post war environment for an enduring peace—but do NOT think about this as a linear process. It will also be about sociology, bureaucratic politics, the role of the media, economics, health care, power…. Most of all this semester it will be about the transformational nature and effects of ROBOTICS, AUTONOMOUS SYSTEMS, and ARTIFICIAL INTELLIGENCE (RAS/AI) on security and the budget pressures on the national security/ defense budgets—and where to consider taking acceptable risks—geographically and functionally and force posture wise (for example, do we need a $1Trillion nuclear modernization program; or 2400+ F-35s; or 12 carrier battle groups?). THIS AGENDA NOW IS BEING SHAPED GOVERNMENYS and the PRIVATE SECTOR—COMMONLY KNOWN AS THE 3rd OFFSET. (The roots of this can be found in Secretary Hagel’s 214 Innovation Initiative. http://www.defense.gov/News/Article/Article/603658).
INTS 4500 Social Science Methods (4 Credits)
Prerequisites:The course presumes a basic competence in statistics, social science, international relations, and comparative politics. This is an advanced, fast-paced course that seeks to provide students with a sensitivity to research design choices, both for designing their own projects and as critical consumers of the works of other scholars. The course is primarily intended for Ph.D. students at the pre-dissertation prospectus stage as well as for advanced MA candidates pursuing thesis projects. The course content covers diverse methodological approaches from the discipline of Political Science as well as methods from other fields. The course will cover topics including: research questions and ‘puzzles’ in political and social science; causality and causal inference; theory construction; measurement; the comparative method; case selection; and quantitative and qualitative methods. Students should enter the course with several research interests in mind since the final project for the course entails producing a research design that could serve as the basis for a future prospectus. The class sessions will include a formal introduction to different methods, a discussion of readings, and work-shopping of student work. We will also informally discuss tips and tradeoffs in the academic profession and for publishing. The class meetings will rely heavily on student participation and peer critique. At the end of the course, students should be able to identify the strengths and weaknesses of different research designs.
INTS 4575 Systems Thinking for Social Scientists (4 Credits)
The purpose of this course is to introduce students to systems thinking as an approach for understanding and analyzing real-world issues. In addition to introducing the basic principles of systems thinking, questions that well be addressed include: Why do systems behave the way they do? Why do systems resist change and often end up getting worse when we try to change them? How do you find points of leverage within a system? This course uses examples drawn from a range of issues across the field of international studies. In doing so, it illustrates haw a systems perspective can allow you to see parallels between seemingly disparate issues. This course introduces both qualitative and quantitative approaches for analyzing systems and discusses the benefits and limitations of each. Quantitative, computer-based modeling is used in this course, but no background is required.
INTS 4579 International Futures (4 Credits)
Futures forecasting involved decisions about priorities. Decisions require forecasting the trajectory of a society with and without interventions of various kinds. This course involved students in the forecasting and analysis process. In the lab, students learn to use the International Futures (IFs) forecasting system. That system represents multiple issue areas (demographics, economics, energy, agriculture, education, health, socio-political, and environment subsystems) and is supported by a very large database. Students study the structure of each of these modules, learn how they represent the underlying subsystems, how they are linked to other subsystems, and what they tell us about the processes of change globally and in countries and regions around the world. Students use the system for forecasts and analyses of their own.
INTS 4630 Civilian Protection in Armed Conflicts (4 Credits)
Studies of armed conflict tend to focus on the production of violence to the neglect of how civilians might instead be protected. In this course, we will study how to limit violence against civilians. We will begin with an overview of theories of violence and legal and ethical frameworks governing the use of force. We will then consider how various actors throughout society, from state actors, to international actors, to illegal arms actors, to NGO's, to civilians and their communities--the would-be victims of violence--can either promote or restrain the use of violence. We will also consider the conditions under which the protection of civilians is most feasible as well as research methods for analyzing populations and their protection strategies. In their final projects, students will analyze the threats of violence faced by a particular population and design appropriate protection strategies and polices to deal with them.
INTS 4644 Human Rights Research Methods (4 Credits)
This course is about how social science research can be used as a tool to understand and promote human rights. The field of human rights is bedeviled by several challenging obstacles to research, including reporting bias, hidden abuses, missing data and politicization of the facts. To deal with these obstacles, we learn about various methodological tools and how they are applied for the analysis of special human rights topics. By the end of the course, students are equipped to compile and present information to highlight patterns of rights abuses and identify patterns of cause and effects.
This is an advanced topics course centered on International Security students gaining, developing, and practicing their professional skills (specifically research and analytics, integration of creativity, academic material, and analysis, peer to peer leadership and coordination, project management and collaborative tools, and communications) via engagement with material/techniques associated with as well as the actual development and execution of a group based professional grade defense/security policy analysis. While the class will contain some traditional academic elements to provide all participants with an enhanced tool kit of skills and analytic options, the bulk of the class takes place through the development of the group defense/security policy analysis executed by 6 person student Project Teams that will be developed through an iterative process over the course and then presented to a group of defense and security professionals for their appraisal. Through this process, security students will be able to get a sense of how real world projects are developed and executed as well as the challenges that confront the production thereof.
INTS 4676 Advanced Topics in Security (1-4 Credits)
This is an advanced topics course centered on International Security students gaining, developing, and practicing their professional skills (specifically research and analytics, integration of creativity, academic material, and analysis, peer to peer leadership and coordination, project management and collaborative tools, and communications) via engagement with material/techniques associated with as well as the actual development and execution of a group based professional grade defense/security policy analysis. Prerequisites: INTS 4735.
INTS 4735 Defense and Security Methods (4 Credits)
The purpose of this overview course in defense analysis methods is to provide students with the foundations to successfully conduct research and analysis in defense-related topics, whether within the national security community, in academia, or as a contractor. This course should also help prepare the student to complete his or her Master's thesis. The course aims to improve the student's ability to comprehend and assess the graduate-level readings assigned in other courses, and to write research papers and complete other written assignments for those courses. The course is intended to provide take-away skills that can be applied to professional activities after graduation: in particular, students should have greater confidence in their abilities to locate, read, commission, design, or conduct relevant research, and to draft research proposals. This class focuses on methods employed in both policy analysis and the social sciences. The emphasis is on qualitative rather than quantitative methods.
PPOL 4852 Executive International Economic Policy (4 Credits)
This course focuses on understanding how governments design and implement economic policy and how economic, social, political, and cultural forces impact on that process. The course centers on key debates around major government policy decisions and analyzes the alternative paths open to policymakers at the time at which they took those decisions. We will emphasize central questions in macroeconomics as well as international and development economics through a case-study lens focused on evaluating decisions by assessing the arguments in favor of and against various policy alternatives. In the discussion of these cases, we will contrast neoclassical economic theories of optimal policy design with political economy, structuralist, and institutional views of the policymaking process. Through each example, we will engage with the views, constraints, and motivations of key actors and groups that influenced the policy formation process. Topics covered include global financial and health crises, fiscal deficits, structural adjustment, the role of multilateral organisms, high and runaway inflation, the causes of underdevelopment, economic sanctions, debt limits, the spread of globalization, financing climate action, and the reliability of economic data.
PPOL 4950 Policy Memorandum (4 Credits)
The Policy Memorandum research project is designed to provide the MPP student with a capstone experience that will synthesize the knowledge and skills that were acquired during the 60 quarter hours of formal coursework. Included among the skills that students will apply are research, quantitative methods, economic analysis, cost-benefit analysis, budgeting and project management.
PPOL 4995 Independent Research (1-8 Credits)
The Policy Memorandum research project is designed to provide the MPP student with a capstone experience that will synthesize the knowledge and skills that were acquired during the 60 quarter hours of formal coursework. Included among the skills that students will apply are research, quantitative methods, economic analysis, cost-benefit analysis, budgeting and project management.
CNP 5771 Counseling Psychology: Doctoral Research Seminar (3 Credits)
The purpose of this course is to guide students in completing the Dissertation. This is not a research methods course but a course focused on the application of research understandings, knowledge, concepts, and terminology in the design of a dissertation. It is assumed that prerequisite research courses provide students with considerable information, foundational knowledge, and conceptual understandings of both quantitative and qualitative research methodologies and other relevant topics.
COUN 4810 Comprehensive School Counseling Programs (4 Credits)
This course provides a framework for developing a comprehensive school counseling program in order to meet the development needs of students in the domains of academic, career, and social/emotional development. Students become familiar with the American School Counselor Association’s National Model, the use of data to inform programmatic decision making, and factors related to school attendance and safety. The course is designed to provide students with practical experience in needs assessment, and program development, implementation, and evaluation. Prerequisites: COUN 4730 and COUN 4740.
COUN 4815 Program Evaluation (2 Credits)
This course facilitates familiarity with application and implementation of program evaluation concepts, including evaluation design, statistical methods, and ethical and cultural considerations. Prerequisites: COUN 4630, COUN 4730, and COUN 4740.
ADMN 4812 Perspectives in District Leadership (4 Credits)
District leaders must focus their actions on the common goal of improving student learning and school systems must be organized to make this the fundamental priority. The purpose of this course is to examine district-level leadership, policies, and practices that support a school community committed to and focused on achievement of all students. The district role is emphasized in supporting school improvement, closing achievement gaps, providing resources, monitoring and using accountability data, and working with the community and school board leadership. Responsible administration of human and fiscal resources is necessary to accomplish systemic instructional improvement at the district level. The goal is to prepare leaders who will lead school districts that are culturally responsive and promote equity and excellence. This course includes an experiential learning component.
ADMN 4821 Improvement Science (4 Credits)
The course focuses on school reform and improvement through improvement science. Improvement science is an emerging concept which focuses on exploring how to undertake continuous quality improvement. The aim of this class is to explore strategies of improvement science to develop educators' knowledge and skills to uncover and use data that exist in classrooms and schools for the purpose of promoting educational change and improvement. The participants in this course will create and conduct an improvement science project. This course includes an experiential learning component.
The purpose of this course is to understand organizational culture as a complex and challenging issue to shape and lead. The complex culture of schools or other educational organizations means many things including climate, organizational members’ engagement, culturally competent practices and the quality of human relationships in the organizational environment. This course will enable leaders to analyze the components of an educational organization’s culture and develop specific plans to create a culture that supports improved learning outcomes for every student, using high-quality, best instructional practices. Following the collection and analysis of data, students will be prepared to serve as Equity Oriented Change Agents (EOCA), leading the improvement of school culture focused on equitable access to high-quality instruction and services for every student. This course includes an experiential learning component.
ADMN 4841 Instructional Leadership for Equitable Schools (5 Credits)
This course serves aspiring principals in the development and application of skills and knowledge associated with standards-based instructional practices, curriculum planning and development, assessment, and program evaluation. Students are assisted in developing and understanding issues of diversity and multiculturalism and their influence on the development and supervision of the instructional program. Although the major focus is on local aspects of standards-based education, some attention is given to the national role in this area. School leaders need to apply quantitative and qualitative research skills in a variety of ways to understand and improve the work of schools. This course reviews methods, applications, and data sources, including assessments and large-scale datasets, for continuous school improvement and program evaluation. In addition to the issues of instructional leadership, considerable attention is given to the examination of the needs of the individual student in the learning environment as well as research on learning styles, learning theories and models of teaching. Primary focus areas are supports for special education students, English Language learners, gifted students, and students in poverty. Students must be accepted into an ELPS certificate or MA program.
HED 4202 Program Evaluation in Higher Education (4 Credits)
This course is an overview of the craft of program evaluation, “…the systematic assessment of the operation and/or the outcomes of a program or policy, compared to a set of explicit or implicit standards, as a means of contributing to the improvement of the program or policy” (Weiss, 1998, p. 4). Program evaluation, simply put, is the craft of applying research methods in a thoughtful way to the task of finding out what and/or how interventions work in the context of the programs or policies in which they operate. This is accomplished by systematically investigating the effectiveness of program processes and outcomes within their political and organizational context. The goal is to inform social action and, by extension, improve conditions for program recipients and participants. Students in this course will explore program evaluation within the context of higher education. The purpose of this course is threefold: (1) Develop an understanding of existing evaluation theory and practice; (2) Apply evaluation theory and approaches to the context education evaluation; and (3) Develop an experiential base upon which to engage in evaluation in educational practice, and for many as a component of doctoral research projects.
HED 4212 Introduction to Public Policy and Higher Education (4 Credits)
This is an introductory course that gives students an overview of federal and state public policy, current issues, research methods, and practical skills required for the policy formation process. This is the introductory seminar to the Public Policy, Leadership and Organizational Change emphasis area for the master's program.
LIS 4060 Reference (3 Credits)
Information resources include a number of different kinds of reference materials in a wide variety of formats. These include guidebooks, encyclopedias and dictionaries, indexes and abstracts, handbooks, bibliographies, biographical finding tools and biographies, data sets and much more. Many of these resources are available on-line, as well as in print and other digital formats. This course will help students identify and evaluate the most likely resources for information queries in particular settings. It will also provide the opportunity to find answers to real research questions. The course will cover the primary resources for the broad disciplines of business, humanities, sciences, social sciences and government publications in print and electronic formats. Class exercises will reflect the multidisciplinary and multicultural interests and characteristics of library users. Prerequisite: LIS 4015. Recommended prerequisites: LIS 4000 and LIS 4011.
LIS 4206 Web Content Management (3 Credits)
This course will include instruction in web page creation, selection, and evaluation of web content as well as web site management. Selection of web page content will be discussed in the context of organizational knowledge management and competitive intelligence needs. Differences in information needs for provision of public information and competitive intelligence on Internet pages versus the organizational information needs of Intranets in knowledge management will be explored. This course also will address human-computer interface design to allow web page designers to create effective web pages according to established principles of design.
LIS 4210 Data Visualization (3 Credits)
This course provides a practical introduction to the principles, theories, and applications of information visualization in the research data context. This course contextualizes modern practices in information visualization by examining historical approaches to visualization with an eye on theories that inform contemporary visualization best practices. Using a hands-on component, students will get real-world experience in visualizing datasets, and building visualization dashboards that integrate multiple visualizations.
LIS 4510 Children's Materials and Services (3 Credits)
This course is designed to prepare librarians to work with children (ages birth to 12 years) in school and public libraries. Topics covered include children's development, reading interests and needs, materials selection, collection development (including print and non-print materials), discussions of specific genres, reading motivation skills, designing a children's area, and developing various programming ideas. Students read/view/listen to and evaluate a wide variety of materials for and about this age group, prepare and present booktalks and stories, become familiar with review sources, and design a one-year plan for youth services in a school or public library.
LIS 4520 Young Adult Materials & Services (3 Credits)
This course prepares librarians to work with young adults (ages 12-18) in school and public libraries. Topics covered include young adult development, reading interests and needs, materials selection, collection development (including print and non-print materials), and discussions of specific genres, reading motivation skills, designing a YA area, programming, and intellectual freedom issues. Participants will read/view/listen to and evaluate a wide variety of materials for and about this age group, prepare and present booktalks, become familiar with review sources, and design a one-year plan for a YA department in a small school or public library.
LIS 4810 Digital Libraries (3 Credits)
This course provides a theoretical foundation for the study of digital libraries and discusses the technological, organizational, social, and legal issues associated with the development and use of digital libraries. Through this course students develop an understanding of digital library components and explore theoretical and practical approaches to constructing, maintaining, and evaluating digital libraries. Topics examined include digital library definitions, design and architecture of digital libraries. Topics examined include digital library definitions, design and architecture of digital libraries, information access in the digital library environment, digital library users and user services, data repositories, digital curation, digital preservation, digital library evaluation, and digital librarianship.
LIS 4850 Digital Preservation (3 Credits)
Students will learn the principles and practices of preserving access to information encoded in digital form. They will learn how to assess digital preservation needs within an institution, write digital preservation policies, and how to collect and present data to make a case for acquiring funds for digital preservation activities. Students will learn the basics of digital information encoding as it applies to the technological aspects of digital preservation, and will learn about current tools and practices used to preserve access to digitally encoded information over time. The course will be a combination of lecture, discussion, and problem solving. It requires participants to conduct independent research and writing. Critical reading of course materials is essential to stimulate active participation in class discussions.
RMS 4900 Education Research and Measurement (4 Credits)
This course is intended for Master's degree students in the College of Education. Quantitative research designs, empirical methods of data collection and interpretation, and measurement issues in research are examined.
RMS 4913 Multivariate Analysis (5 Credits)
Conceptual and applied analyses of common multivariate statistical techniques used in research in social sciences are presented as are assumptions and limitations of techniques and interpretation of results. Cross listed with SOWK 5950. Prerequisite: RMS 4911 or RMS 4912.
RMS 4919 Topics in Statistics (1-5 Credits)
Topics vary by quarter but may include log-linear analysis, factor analysis, or missing data analysis.
RMS 4929 Topics in Psychometrics (1-3 Credits)
Topics vary, but include: large scale testing, computer applications of item response theory, affective measure construction, generalizability theory, additive conjoint measurement, and standing testing. Prerequisite: RMS 4921 or instructor permission.
RMS 4930 Quantitative Research Design (3 Credits)
This course provides in depth study of empirical research methods involved in experimental, quasi-experimental, single-subject, and non-experimental quantitative research designs.
RMS 4939 Topics in Quantitative Research Methods (1-5 Credits)
Topics vary, but include minimization as an alternative to randomization, propensity score modeling as an alternative to experimental control, and analysis of data from single-subject designs. Prerequisites: RMS 4930 or instructor permission.
RMS 4942 Qualitative Data Collection and Analysis (4 Credits)
In this intermediate level qualitative research course students learn about design, purposeful sampling, field work, observational approaches, and interviews, with special attention directed to the skills and competencies needed to gather and analyze high quality data. Prerequisite: RMS 4941 or instructor permission.
RMS 4948 Criticism and Connoisseurship: Qualitative research and the enhancement of practice (3 Credits)
Qualitative inquiry in educational settings takes many forms: ethnography, grounded theory, case-study research, and more. What these methods have in common is a framework built upon social science. Criticism and connoisseurship, however, draws its conceptual underpinnings from the arts and humanities. What does it mean to have a conceptual framework dependent upon the arts? How are the methods of educational criticism different from other research methods? This class teaches students how to conduct research using this method and it provides responses to these types of questions in order that students can defend this type of research as well as others that depend on the arts and humanities as their basis. Prerequisite: RMS 4941.
RMS 4949 Topics in Qualitative Research (1-5 Credits)
This seminar builds on the content of other qualitative research courses offered in the RMS program and meets the students where they are on their dissertation journey; thus learning opportunities are tailored to individual needs as far as possible. Assignments focus on the issues pertinent to the design of dissertation proposals and writing, including ethical issues and IRB preparation, theoretical/conceptual framework, literature review, methodology, data collection and analysis strategies, and various forms of representation. Prerequisite: RMS 4941.
RMS 4950 Qualitative Research Methodologies (3 Credits)
Each year this course examines three qualitative research methods. The methods that might be covered in any given year include: phenomenology, grounded theory, narrative, case study, and ethnography. For each method, the following is addressed: philosophical and historical foundations, various ways the method has been utilized, and practical recommendations for conducting research utilizing this method. Prerequisite: RMS 4941.
RMS 4953 Topics in Data Management (1-3 Credits)
This is a preparatory course emphasizing the manipulation and analysis of data in electronic form.
RMS 4969 Topics in Program Evaluation (1-5 Credits)
Topics vary, but include advocacy and policy change, assessment in higher education, multi-level evaluation, cost effectiveness analysis, data visualization and reporting, assessment in distance education, and evaluation in the arts and culture. Prerequisite: RMS 4960.
RMS 4980 Practicum in Research (1-5 Credits)
This course provides a supervised experience in design and implementation of an empirical research or evaluation study. Organization of research proposals, completion of human subjects applications, collection, and analysis of data are emphasized. Students are expected to prepare a written report of their project which is suitable for professional presentation or publication.
CFSP 4310 Early Childhood Development (3 Credits)
This course focuses on early childhood development during, from the prenatal period to approximately five years of age. Major theories of early childhood development and research methods for studying infant and early childhood behavior will be discussed. Emphasis will be on the physical, cognitive, communicative, social, and emotional aspects of development, for children who are typically developing, at risk or with special needs. All-inclusive issues, as well as health, risk and protective factors will be addressed. The importance of investing in early childhood programs, fostering nurturing relationships during the early years, and addressing the diverse needs of families will be emphasized. Prerequisites: None.
CFSP 4316 Infant through Adolescent Development (3 Credits)
This course focuses on early childhood development from the prenatal period to approximately five years of age. Major theories of early childhood development and research methods for studying infant and early childhood behavior will be discussed. Emphasis will be on the physical, cognitive, communicative, social, and emotional aspects of development, for children who are typically developing, at risk or with special needs. All-inclusive issues, as well as health, risk and protective factors will be addressed. The importance of investing in early childhood programs, fostering nurturing relationships during the early years, and addressing the diverse needs of families will be emphasized.
CFSP 4323 Psycho-Educational Assessment III (4 Credits)
This course is the second of two required courses designed to provide students in School Psychology with expertise in individual intelligence and achievement test administration, scoring, interpretation, and report writing. Each student has an opportunity to administer various cognitive and achievement measures, with particular emphasis on the Woodcock Johnson Scales. Nontraditional forms of assessment, as well as adaptive behavior measures, are also covered. Integrating results of assessments with other data to provide effective educational recommendations continues to be an emphasis. The focus of the class is on the assessment of school-aged children. Lab fee required. Prerequisites: CFSP 4312, 4322.
CFSP 4324 Psycho-educational Assessment IV (3 Credits)
This course is designed to provide students with knowledge of the major approaches to assess a school-aged student's social and emotional status. Instruction includes underlying theories, use and interpretation of interviewing techniques, observation methods, objective behavior ratings, self-report measures, sociometric procedures and selected projective measures. Emphasis is placed on the integration and interpretation of multimethod, multisource and multisetting data to improve diagnostic accuracy, and the use of assessment results in developing effective intervention strategies. Students learn to incorporate such assessment information using case studies. In addition, students develop skills in writing case reports and in making effective presentations of social-emotional assessment results. Consideration is given to contemporary issues in the assessment of children's social emotional functioning. Lab fee required. Prerequisites: CFSP 4310, 4311, 4322, 4323.
CFSP 4363 School Psychology Program Development and Evaluation (3 Credits)
This course focuses on theory and practice of program development and evaluation in school and community agency settings. Both qualitative and quantitative methods of program evaluation are discussed. Students have the opportunity to collaborate on a comprehensive evaluation of a specific educational, health, or mental health program. Prerequisites: RMS 4910, CFSP 4332.
CUI 4032 Analysis of Teaching (3 Credits)
Provides a systematic introduction to the research base that characterizes effective practice and to the array of research methods that can be employed to study teaching and teacher development.
CUI 4058 Teacher as Researcher (3 Credits)
Emerging philosophical and methodological issues that arise when school practitioners undertake research within their own sites; range of research traditions including quantitative, statistical research and qualitative methodologies; mastering relevant skills and accessing resources for students to be better prepared to conduct their own inquiries and understand and solve problems.
CUI 4310 Supporting Apprentice Teachers (3 Credits)
This course is designed to support mentor teachers as they build relationships with the apprentice teachers during the residency or student teaching phase of a teacher education program. It is designed to complement the professional standards-aligned coursework that apprentice teachers complete as part of the licensing process. As such, this course is designed to strengthen the connection between the institute of higher education and the field placement site. In doing so, mentor teachers can support the learning of apprentice teachers in the practical application of theoretical frameworks. This course specifically addresses supporting apprentice teachers as they learn as they learn to create culturally responsive classroom environments and to address the strengths and needs of all students particularly Culturally and Linguistically Diverse (CLD) Learners and students in Special Education. Topics addressed include mentor support for apprentice teachers’ planning, teaching of reading and writing, applying a teacher evaluation framework, designing and interpreting formative and summative assessment, using data to inform instruction and differentiation for ELLs and GT identified students.
CUI 4313 Supporting Apprentice Teachers IV (3 Credits)
This course is designed to support mentor teachers as they build relationships with the apprentice teachers during the residency or student teaching phase of a teacher education program. It is designed to complement the professional standards-aligned coursework that apprentice teachers complete as part of the licensing process. As such, this course is designed to strengthen the connection between the institute of higher education and the field placement site. In doing so, mentor teachers can support the learning of apprentice teachers in the practical application of theoretical frameworks. This course specifically addresses supporting apprentice teachers as they learn about using data to inform instruction, differentiating instruction for English Language Learners and Gifted and Talented identified students, and developing competencies in the evaluative framework for residency or student teaching used in a teacher education program.
CUI 4407 Current Issues in Gifted Education: Identification (3 Credits)
This course focuses on the screening and selecting of gifted and talented students. It is designed for practicing professionals - teachers, counselors, psychologists, and administrators - who must make decisions about the identification and serving of gifted and talented students. Course uses multiple assessments, both quantitative and qualitative, to identify gifted students within an increasingly diverse population (including culturally- and ethically-diverse, high-potential, linguistically-different students with unique affective needs as well as high-potential economically-disadvantaged students). Students will use data to diagnose educational needs, prescribe appropriate educational strategies and to incorporate appropriate identification strategies for identifying gifted and talented students. Legal responsibilities and parent communication as well as staff development are emphasized. Students are required to develop an identification model based on relevant theory and current practices to be used in their particular setting.
Enforced Prerequisites: CUI 4400 with a minimum grade of C- OR CUI 4401 with a minimum grade of C-.
CUI 4408 Creativity: Theory & Practice (3 Credits)
The essence of innovation is creativity, in thought, process and outcome. Classic and current theories provide a foundation for analysis of the concept of creativity. This course is designed to provide participants with an understanding of 1) the conceptual foundations and definitions of creativity; 2) how intelligence, creativity, and non-intellective factors are related to the constructs of giftedness; 3) documented brain research underlying exceptional cognition and/or creativity; 4) principles and issues in the identification and appropriate programming for creative individuals; and 5) the multiple perspectives and manifestations of creativity. Salon discussion groups, lectures, class activities and assigned readings and projects focu on the history and nature of the construct of creativity, theories of creativity, the role of innovation and transformation, assessment and measurement tools, environmental support of the creative process and creativity, and teaching and learning applications.
CUI 4412 Culturally and Linguistically Diverse Learners in Gifted Education (3 Credits)
Culturally and Linguistically Diverse Learners have inequitable access to gifted programming, curricula and services; therefore, they do not receive instruction that nurtures their learning talents, culture, and emergent bilingualism. This course is designed to address the needs of the Culturally and Linguistically Diverse (CLD) and the Gifted and Talented (GT) learner. Upon completion of this course, students should be able to meet the approved standards for the English Language Learner Professional Development Pathway. In addition to ELL standards, this course is aligned with state gifted education standards.
CUI 4455 Assessment of Students with Special Needs (3 Credits)
Theories, research, effective practices and background information needed to develop, implement, analyze, and apply assessment data for mild/moderate-needs students.
CUI 4536 Language and Cultural Issues in Assessment and Instruction (3-4 Credits)
This course expands the educators' knowledge of the connection between data-based instruction and assessment. Educators assess student learning by utilizing strategies that provide continuous feedback on the effectiveness of instruction. Educators learn informal and formal assessment practices that promote student learning and achievement. Educators develop knowledge and understanding of initial assessment of culturally and linguistically diverse learners' skills and abilities in order to provide appropriate placement and instruction. Educators utilize native language tests to promote adequate placement/transition of students. This course also helps educators develop a framework to analyze and develop culturally responsive assessment practices in order to improve student achievement. Finally, this course integrates Response to Intervention (RTI) strategies to improve student assessment.
CUI 4541 Curriculum, Instruction, and Assessment: Theory and Practice II (1-4 Credits)
In this course, apprentice teachers will explore the theoretical underpinnings and practical application of data and assessment. This course will build essential knowledge and skills in school and classroom assessments, research methodology, and equity in assessment practices. Students will design a series of assessments aligned to unit goals. Course restricted to students in TEP program or instructor approval.
CUI 5980 Research as Problem Analysis (3 Credits)
This course is the first of three culminating research courses for students in the Ed.D. in Curriculum and Instruction. This course is designed to guide candidates through the doctoral proposal process and introduce the initial stages of data collection and analysis.
CUI 5983 Defense of Research (1 Credit)
This course will build on the “persistent problems of practice” and research questions identified in CUI: 5980, data collection and analysis in CUI 5981, and applied research skills in CUI 5982. By the end of the course you will be able to schedule and present your doctoral research project for defense. Completion of CUI: 5982 "Research as Applied Research" or permission of the instructor.
BIOL 3045 Coral Reef Ecology Lab (1 Credit)
Ecology of coral reefs laboratory to supplement lecture material; travel to the Caribbean over spring break to observe coral reefs firsthand; introduction to research methods. SCUBA certification and permission of instructor required. A travel and dive fee is associated with this course.
BIOL 3055 Ecology of the Rockies (4 Credits)
A week in residence at the Mt. Evans Field Station prior to the start of fall quarter includes field projects dealing with ecology and environmental issues. On campus classes involve data analysis and interpretation and formal scientific communication. Themes include terrestrial and aquatic ecosystems, taxonomic groups ranging from conifer stands to aquatic insects and mountain goats. Lab fee associated with this course. Prerequisite: BIOL 2010 or permission of instructor.
BIOL 3070 Ecological Field Methods (4 Credits)
Series of field exercises for students to learn principles and procedures of field methodology, data analysis and technical writing in ecology; problems drawn from population, community and ecosystem ecology. Lab fee associated with this course. Prerequisite: BIOL 2010.
BIOL 3251 Exercise Physiology (4 Credits)
This course will cover exercise physiology topics included but not limited to: energy systems, physiological response to exercise/training, and exercise programming. A strong background in human physiology is recommended. This course counts as a category elective for the Physiology in Health and Disease major. Prerequisite: BIOL 1010.
BIOL 3410 Animal Behavior (4 Credits)
This class examines animal behavior from an evolutionary and ecological perspective. The course provides the background needed to understand behavioral evolution, including a focus on the inheritance of behavior, natural selection, sexual selection, and kin selection. This class studies the evolution of a variety of behaviors, including communication and displays, mate choice, parental care, cooperation, mating systems, social behavior, habitat selection, foraging, and anti-predator behavior. The emphasis is on theoretical principles, design of experiments, and interpretation of data. This course counts as a category elective for the Ecology and Biodiversity major. Prerequisites: BIOL 1010 and BIOL 1011, and BIOL 2010. Recommended Prerequisite: BIOL 2090.
BIOL 4212 Advanced Molecular Biology (3 Credits)
This course focuses on a detailed analysis of regulated gene expression. The topics include lectures and readings of relevant literature in areas covering gene regulation at multiple steps, including transcription, RNA processing, and translation. In particular, the logic of experimental design and data analysis are emphasized.
BIOL 4231 Responsible Conduct in Rsrch (1 Credit)
This course covers several topics regarding guidelines for ethical practices in research. Topics include: data ownership, conflict of interest and commitments, human subjects, animal welfare, research misconduct, authorship, mentoring, peer review, and collaboration. The course includes an online training component and meets one hour each week to discuss these topics.
A survey of environmental toxicology concepts: animal testing, dose-response data, epidemiology, risk assessment. The course includes ecotoxicology, focusing on the alteration of biological and chemical systems beyond the simple response of an individual to an environmental chemical. Prerequisites: (CHEM 2270 or CHEM 2011) and CHEM 2453.
CHEM 3610 Physical Chemistry I (3 Credits)
Fundamentals of thermodynamics, including phase and reaction equilibria, properties of solutions, and electrochemistry needed for advanced study in life sciences and for Physical Chemistry II and III. May be taken for graduate credit by nonchemistry majors. Prerequisites: CHEM 2453, calculus and physics.
CHEM 3621 Physical Chemistry III (3 Credits)
Fundamentals of kinetic theory and statistical mechanics. May be taken for graduate credit by nonchemistry majors. Prerequisite: CHEM 3620.
GEOG 3130 GIS Programming with Python (4 Credits)
This advanced course explores the more technical aspects of GIS functions and data structures. Students have hands-on access to both raster (grid-cell) and vector-based software packages in the form of lab exercises that culminate in a small student-designed GIS project. Prerequisite: GEOG 2100.
GEOG 3200 Remote Sensing (4 Credits)
This course acquaints students with the basic techniques of the collection, processing and interpretation of information about the character of the earth's surface from remote locations. Students become familiar with the use of the visible, infrared, thermal and microwave portions of the electromagnetic spectrum as a means of determining land cover and/or land use. Both manual and computer-assisted techniques are discussed and include hands-on applications.
GEOG 3230 Advanced Remote Sensing (4 Credits)
This course will build on the basic remote sensing concepts presented in GEOG 3200. Students will explore more in-depth concepts relevant to satellite and airborne remote sensing, including radiative transfer and information extraction. In addition, students will be introduced to two cutting-edge sources of data about the Earth's surface: hyperspectral and lidar (Light Detection and Ranging) sensors. Students will study specific applications of advanced digital image processing techniques for environmental monitoring, natural resource management, and land-use planning. Finally, students will integrate remote sensing and other spatial datasets in the context of Geographic Information System (GIS) analysis. Prerequisite: GEOG 3200.
Nature, magnitude, sequence and causes of Pleistocene and Holocene climatic changes; effects of climatic change on plant/animal distributions and human populations; paleoclimatic research methods. Laboratory and field trips. Prerequisites: GEOG core, ENVI 3000.
GEOG 3610 Climatology (4 Credits)
Climatology is the study of the processes that result in spatial and temporal variation of weather. This course introduces the student to the processes responsible for the transfer of matter and energy between the Earth's surface and the atmosphere and the average weather conditions that result. In addition, topics of global concern, such as greenhouse effect, El Nino, urban heat islands and acid rain, are discussed. Laboratory exercises provide an opportunity to investigate climate variation and climatic change through the use of a variety of computer simulations. Prerequisites: GEOG 1201, GEOG 1216, & GEOG 1264.
GEOG 3755 Geography of Health (4 Credits)
The geography of health is a thriving area of study that considers the impact of natural, built, and social environments on human health. This course introduces students to three geographical contributions to health studies. First, it emphasizes the importance of ecological approaches to health, which consider interactions between humans and their environments, including topics such as how climate change might influence disease distributions, and how the built environment can influence patterns of physical activity. A second focus is social theory, exploring how aspects such as race, socioeconomic status, and identity play a critical role in influencing human health. A third section of the course considers how spatial methods (cartography, GIS, and spacial statistics) can help answer health-related questions.
GEOG 3955 Pollen Analysis Seminar (3 Credits)
Pollen grains preserved in sediment provide long-term records of vegetation conditions. Changing proportions of pollen types may reflect climatic fluctuation or human impacts. We review important recent research in pollen analysis (palynology), pollen sampling, laboratory techniques and pollen identification. Students are responsible for counting a number of samples and contributing data for a pollen diagram.
GEOG 4000 Fundamental Geographic Perspectives (4 Credits)
A foundation course for persons in the community, without a degree in geography, who want to purse an education in or make use of computer-based geographic technology but who need a foundation in geographic concepts and perspectives.
GEOG 4020 Geographic Research Design (4 Credits)
This class prepares you to undertake creative geographic research leading to the generation of new knowledge. You will produce a NSF-style proposal by the end of the class. In this class, we focus on your idea generation and proposal writing rather than philosophy or specific methods. Specific objectives of the course include providing you the following skills: 1. The ability to create and communicate scholarly work in writing and orally 2. The ability to critique your own work and the work of other in a constructive fashion 3. Incorporation of the core ideas of geography and your field into your research 4. An understanding and appreciation of the various research methods in geography and other cognate disciplines. This understanding includes a discussion of ethics in research. 5. Ability to write an effective vita, statement of area of specialization, literature review, problem statement, and research proposal.
GEOG 4420 Urban and Regional Planning (4 Credits)
The field of urban and regional planning is concerned with the future of cities, neighborhoods, metropolitan areas, and extended regions. How do local governments (cities, counties) and metropolitan planning organizations (regional planning agencies, councils of governments) work with community stakeholders (neighborhood associations, chambers of commerce, businesses, citizens, non-governmental organizations) to formulate plans that will guide the future development of a city and its region? Cities and their regions face numerous challenges including population and employment growth or decline, economic development, neighborhood vitality, housing availability and affordability, urban design, land use, transportation, sustainability, access to parks and open space, air quality, floodplain management, water resources, and social equity among many others. How places address these challenges is critical to the future health and livability of our cities, neighborhoods, metropolitan areas, and extended regions. This course will have a community-engaged service learning component. Community-engaged scholarship and teaching comprise intellectually and methodologically rigorous work that is grounded in the norms of democratic education: inclusiveness, participation, task sharing, reciprocity in public problem solving, and an equality of respect for the knowledge and experience that everyone involved contributes to education and community building. The specific service learning project for the class will be to assist the Metropolitan Denver Nature Alliance (Metro DNA) with its goal to increase the community’s engagement with nearby nature by reviewing, analyzing, and collecting data from park/ open space plans of cities and counties in the Denver metropolitan area. Cross-listed with GEOG 3420.
GEOL 3540 Hydrology (4 Credits)
This course provides an overview of the hydrologic cycle with emphasis placed on the study of applied hydrology. Discussions include the fundamental characteristics of precipitation, runoff processes, calculation of flood hazards, aquifers (porosity and permeability), the geologic settings of groundwater, the basic physics of groundwater flow, and water supply and use. Prerequisite: GEOL 1010, GEOG 1203 or instructor's permission. Recommended prerequisite: one introductory statistics course.
MATH 3060 Mathematical Logic (4 Credits)
Classical propositional calculus (deductive systems and truth-table semantics), first-order logic (axiomatization and completeness), elements of recursion theory, introduction to nonclassical logics. Prerequisite: MATH 2200.
MATH 3720 Coding Theory (4 Credits)
Goals of coding theory and information theory, instantaneous and Huffman codes, Shannon theorems, block and linear codes, generating and parity-check matrices, Hamming codes, perfect codes, binary Golay code, Reed-Muller codes, cyclic codes, BCH codes, Reed-Solomon codes, ideas of convolutional and turbo codes. Prerequisite: MATH 3170.
Ideals, left and right R-modules, simple modules, totally decomposable modules, Wedderburn-Artin theorems, Artinian and Noetherian rings and modules, Hopkins theorem, Hilbert basis theorem, free modules, projective and injective modules, Kaplanski theorem. Prerequisites: MATH 3176 or MATH 4176.
MATH 5000 Doctoral Seminar (3 Credits)
Techniques, methods used in mathematical, computing research. Includes proofs, bibliographic searching, writing styles, what constitutes an acceptable dissertation.
PHYS 3841 Thermal Physics I (4 Credits)
First of a two-quarter sequence. Laws of thermodynamics; thermal properties of gases and condensed matter; kinetic theory of gases, classical and quantum statistics. Prerequisites: PHYS 1113, PHYS 1213 or PHYS 1214 and MATH 2070.
PHYS 4001 Introduction to Research I (1,2 Credit)
This course is the first of the 3-course sequence designed to provide the opportunity of learning fundamental skills to conduct independent research in any physical science discipline. In this course, students review essential material in mathematical physics, learn basic programming techniques and improve upon their skills in literature search and scientific writing, especially proposal writing. Special in-class seminars in collaboration with the Penrose Library and Writing and Research Center are scheduled. Student are introduced to research conducted by Physics and Astronomy faculty so that they can choose a faculty member with whom to take on a Winter Research Project during the winter interterm and winter quarter as part of Introduction to Research II. Students must prepare and submit a research proposal before the end of the fall quarter.
PHYS 4100 Foundations of Biophysics (3 Credits)
Focus of the course is on application of basic physics principles to the study of cells and macromolecules. Topics include diffusion, random processes, thermodynamics, reaction equilibriums and kinetics, computer modeling. Must be admitted to the MCB PhD program or related graduate program with instructor approval. Cross listed with BIOP 4100.
LAWS 4035 Legal Research for Practice (2 Credits)
Most new lawyers spend the bulk of their time conducting legal research. This course provides students with the practical research skills needed to succeed in today’s law practice settings. The course follows an innovative sequence of classes structured around the most important research methods, tools, and search techniques for legal research proficiency. This approach teaches students the skills they need to be as effective and efficient as possible in their research, regardless of the research platform selected or the type of legal source one wishes to search. This course also uses a problem-based approach designed to simulate the types of research requests made in law practice settings. Course assignments include a variety of exercises, both in-class and outside of class, as well as four research assignments that demonstrate students’ understanding of practice-focused research. Laptops are required for this course.
LAWS 4218 Discovery Practicum (3 Credits)
Most civil litigations never get to trial. Instead, these cases are settled after the discovery period has revealed the strengths and weaknesses in the case. This course focuses on the instruments, rules, and case law governing discovery of information in litigation: interrogatories, document requests, requests for admissions, and depositions. It is taught in the form of a "whole-course simulation," which means students will represent a party and have an opposing counsel in a simulated litigation throughout the course. Students prepare and serve discovery documents (just as in practice), take, defend, and act as a witness in a deposition, and reach a settlement of the case at the end of the course. Because of the nature of the course and the many practice documents prepared during the semester, there is no final examination. This course will satisfy the Upper Level Writing requirement (ULW). This course is a “Carnegie Integrated Course.”.
LAWS 4230 Estate Planning (2 Credits)
Estate analysis, including fact gathering and the analysis of data; the psychological aspects of “role playing” in estate planning; the members of the team (the attorney, the CPA, the life underwriter, the trust officer); life insurance in an estate and business planning context; planning with trusts, including revocable, short-term, and irrevocable; the transfer of a closely held business interest from one generation to the next, including full and partial stock redemptions, cross purchase agreements, private annuity, installment sale, retirement, recapitalization, qualified and nonqualified plans of deferred compensation; special estate planning considerations for the professional corporation, the highly paid executive, and the farmer and rancher; specific cases analyzed.
LAWS 4237 Evidence Practicum (3 Credits)
This practicum is designed to help students build trial skills and make the transition from evidence law learned in the classroom to evidence used in the courtroom. It provides simulation-type experiences requiring students to understand the foundations required to admit different kinds of evidence, to anticipate evidentiary issues, to make and to respond to objections, and to prepare examinations designed to avoid objections. The course supplements Trial Practice by focusing heavily on the rules of evidence. Prerequisite: LAWS 4235.
LAWS 4303 International Criminal Law Practicum (3 Credits)
In this course, the class collectively analyzes the genocide, war crimes and crimes against humanity charges against an accused in a major international tribunal prosecution. Each student is assigned witnesses in the case and is expected to analyze that testimony and record their work in the case database using Casemap software meticulously following previously established protocols. The work involves the students learning the nature of the conflict generally, thoroughly learning the indictment against Taylor, getting up to speed on the law of war crimes and crimes against humanity, and finally assessing the witness testimony for relevant facts and attributing those facts to the legal outline in the case.
LAWS 4390 Law and Neuroscience (3 Credits)
In this survey course, we will cover some neuroscience basics, including a brief history of neuroscience, how neurons and neurotransmitters work, what is currently known about how the brain is organized, both structurally and functionally, how modern neuroscience views the so-called Cartesian dichotomy between emotion and cognition, and the basics of the most common types of neuroimaging. We will then explore the law and neuroscience of pain, memory, lie detection and criminal responsibility, discussing how neuroscientific discoveries might or might not change how the law handles these discrete problems, and the related evidentiary issues of how to get neuroscientific evidence admitted or excluded in cases involving these problems. We will finish, time permitting, with some speculations about artificial intelligence and neuroprosthetics.
LAWS 4562 E-Discovery (3 Credits)
Litigation is undergoing a significant transformation as technology continues to evolve and Society transitions from a “paper” to a “digital” world. That transformation inevitably impacts the legal community, confronting lawyers and clients with the choice of conducting discovery on a pre-computer, “business as usual” basis or embracing the challenges and opportunities presented by “e-discovery.” This course provides students with an understanding of the legal and practical challenges presented by “e-discovery” and how electronically stored information (“ESI”) shapes and impacts litigation and the pretrial process.
The intellectual property capstone is a simulation based course that crosses different intellectual property disciplines. Several different adjuncts are asked to prepare real world problems in different substantive and procedural contexts. The problems vary from year to year, but generally deal with patent, copyright and trademark law from litigation, administrative (i.e. representation before the patent and trademark office) and business perspectives. Students are expected to have taken at least one prior intellectual property course. This course fulfills the experiential component of the IP Certificate requirement.
LAWS 4651 Advanced Legal Writing and Research (3 Credits)
This course provides students with practical experience in drafting various documents they will likely encounter in the practice of law, including both objective and persuasive writing. Students will learn the advanced research skills they need to locate relevant legal materials, including an in-depth knowledge of legal research methods and resources. Students will then use their research results to create precise, clear, effective, and legally sound written documents for a series of modern law practice situations. There will be in-class exercises designed to develop student expertise with particular legal research and writing skills. Course assessments include an internal memorandum, a pleading, a motion, and a decision. This course satisfies the upper-level writing requirement. Laptops are required for this course.
The Criminal Law Clinic Seminar must be taken in conjunction with the Criminal Law Clinic. Class sessions will be devoted to a variety of topics, including classes on lawyering skills, substantive law, issues of lawyering and society, and case review sessions, in which student attorneys will present information about their cases/projects to each other and give and solicit feedback about issues they are confronting in the representation of their clients. The classes include simulation exercises that are critiqued by faculty, and field exercises that involve trips to the Denver jail. Classes are taught by clinical faculty and by guest speakers who include area judges, practitioners, interpreters and other court personnel. Co-requisite: LAWS 4800.
LAWS 5026 Trial Practice III: National Trial Team (3 Credits)
The Trial Teams Course is for the new and veteran students who are selected to represent the school on one of the national trial teams. The course meets one night a week during the summer session for five hours each night. The course is split into two sections, one for returning team members, and one for the newly-selected team members. The course is an advanced courtroom-simulation course in which students work intensely with other students and the instructor, delving into increasingly complex areas of case analysis, evidentiary interpretation and application, examination drafting and presentation, and ethical dynamics of fact patterns. The veteran section begins the first class with students presenting both sides of a criminal case. The new member section begins with refreshers on case analysis, evidence, and courtroom strategies, and culminates with final trials. There is weekly out-of-class case analysis, drafting, and preparation required. Grading is based on classroom participation, written homework, simulated courtroom presentations, and a final trial.
MSLA 4415 Statistics for the Legal Administrator (2 Credits)
This course will introduce the fundamentals of statistics for the legal administrator. Students will learn how to measure efficiencies and work performance, perform and analyze needs assessment, track productivity; measure cases flows, and assess client needs.
MSLA 4901 Law Firm Financial Management (3 Credits)
This course focuses on the key aspects of financial management in a law firm setting. Students analyze financial reports/data, work flow analysis and assessment, understand trust accounts, client billing and internal controls, to ultimately recognize and understand the financial health of the law firm. Prerequisite: MSLA 4410.
TAX 4225 Estate Planning (2 Credits)
This course is a survey of estate planning issues. We will analyze an estate from the beginning through the end of the process. We will examine fact gathering and the analysis of data; the psychological and ethical aspects of working with families at different phases of the estate plan; the members of the team (the attorney, the CPA, the life underwriter, the trust officer); life insurance and retirement planning in an estate and business planning context; planning with trusts; and the transfer of wealth and a closely held business interest from one generation to the next. We will also study ways to plan for future generations and domestic asset protection planning. Estate and Gift Taxation (TAX 4025) is a pre-requisite for the for this course.