Course Descriptions

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Required Courses

 

DATA 5100 - Foundations of Data Science - 3 credits

Introduction to the field of data science. Methodology for identifying, defining, and solving data-driven problems. Ethical, privacy, and security implications of data-intensive applications.

DATA 5111 - Probability for Data Science  - 3 credits

Covariance, multivariate normal distributions, sampling distributions, limit theorems, transformations of random variables, conditional expectation, Bayesian decision and estimation theory. Software simulations.

DATA 5120 - Data Science, Law and Ethics  - 3 credits

Ethical and legal issues in technology and the use of data. Problems, controversies, policies and best practices. Topics include data dignity, data set bias, intellectual property protection of data, technology’s compatibility with social justice values, and ethical technology implementation. Leadership models for data scientists.

CPSC 5070 - Programming for Data Science - 3 credits

Programming, problem solving, basic data structures, algorithms, debugging, and version control using Python.   

CPSC 5071 - Data Management for Data Science - 3 credits

Techniques and systems for ingesting, modeling, and retrieving data. Topics include data cleaning, data integration, data models, and query language.    

DATA 5300 - Applied Statistical Inference and Experimental Design - 3 credits

Statistical inference and experimental design. Parameter estimation, resampling methods, and statistical modeling. Applied linear regression and logistic regression. Selecting appropriate models and interpreting model results. Topics also include causal inference and study designs. Statistical software will be used for computation, simulation, and visualization.

DATA 5310 - Data Visualization - 3 credits

Principles of effective design in data visualization. Psychological underpinnings of data visualization methods. Data wrangling skills to appropriately format and transform data before using visualization tools. How to use visualizations to efficiently communicate information to specific audiences. Accessibility in data visualization.

DATA 5321 - Statistical Machine Learning I - 3 credits

Methods in statistical learning and modeling. Topics will include multiple regression, logistic regression, non-linear models. Dummy variables and interaction effects. Model selection and model assessment.

DATA 5322 - Statistical Machine Learning II - 3 credits

Methods in statistical learning and modeling. Topics include tree-based methods, spline models, support vector machines, neural networks, regularization & dimension reduction, and unsupervised learning.

DATA 5901 - Capstone I - 3 credits

Application of data science in the planning and execution of real-life development project for an industry partner. Student teams define and carry out a data science project from beginning to end. Key steps include: formalizing a question, finding data sources, determining the method of analysis, implementing the analytical procedure, and communicating the results to the client.

DATA 5902 - Capstone II - 3 credits

Application of data science in the planning and execution of real-life development project for an industry partner. Student teams define and carry out a data science project from beginning to end. Key steps include: formalizing a question, finding data sources, determining the method of analysis, implementing the analytical procedure, and communicating the results to the client. 

 

Elective Courses

Choose at least three of these courses, including at least one from List A, Advanced Foundations of Data Science

List A: Advanced Foundations of Data Science

CPSC 5075 - Data Architectures for Analytics - 3 credits

Practical work in data science and analytics depends on efficient access to data from a variety of sources. This course will examine typical corporate data architectures with data warehouses that include both structured and unstructured data. Common storage techniques (including cloud) and retrieval strategies will be included. Advanced SQL, NoSQL, data cleaning, and ETL will also be covered. A start-to-finish implementation of a data warehouse will be developed by student project teams.  

DATA 5155 - Numerical Methods for Machine Learning - 3 credits

Numerical methods for solving machine learning problems. Algorithms for solving supervised and unsupervised learning problems. Programming solutions to machine learning problems. Algorithm convergence properties.

ECEGR 5760 - Advanced Machine Learning - 4 credits

Deep-learning paradigms such as Boltzmann machines and autoencoders. Recurrent neural nets, ensemble learning, bootstrap methods, mixture models, Markov processes.

MATH 5112 - Mathematical Statistics for Data Analytics - 3 credits

Students will study a variety of aspects of statistical estimation, beginning with principles of maximum likelihood estimation and confidence intervals, and then examining properties of statistical estimators such as sufficiency and consistency. Bayesian estimation will be discussed, and contrasted with traditional estimation in terms of both theory and practical use. Hypothesis testing will be examined for the principle of likelihood ratios - examples will focus on inferences about means, variances, proportions, and differences in means and proportions, while emphasizing the ability to generalize these tools to novel problems as well.

List B: Other Data Science Courses

CPSC 5340 - Text Processing and Searching - 5 credits

This course addresses the formal and practical techniques for building Information Retrieval (IR) and text processing systems, including source and query models, evaluation, ranking, and relevance issues, and  efficient indexing and retrieval through search engines.

CPSC 5350 - Social Media Analytics - 5 credits

This course introduces concepts and algorithms that are required for analysis of data available on social media. This course introduces Web techniques, social networks and analysis, network analysis and graph theory, information extraction, link analysis, and Web mining, to study emerging problems with social media.

CPSC 5610 - Artificial Intelligence - 5 credits

Concepts and techniques of artificial intelligence, with an emphasis on building intelligent agents, environments and systems. Methods and tools for building systems that can interact intelligently with their environment by learning and reasoning about the world.

CRJS 5240 - Crime Mapping - 3 credits

Students will learn foundational skills in spatial analysis and crime mapping. Introduction to Geographic Information Systems (GIS) to map and analyze crime patterns. In addition to practical work with GIS, the course will address underlying spatial theories of crime as well as available data sources of socioeconomic and criminal justice related data.

ECON 5305 - Economics and Business Forecasting - 3 credits

Techniques for applied business forecasting with emphasis on time-series methods. A survey of regression-based and time-series methods, models for stationary and non-stationary time series, estimation of parameters, computations of forecasts and confidence intervals, and evaluation of forecasts.

IS 5320 - Web Analytics - 3 credits

This course introduces how to perform web analytics for business. Web analytics includes measuring and analyzing web traffic data such as contents of the web sites, social activities, and advertising performance. It can be used as a tool for conducting market research, and assessing/ improving the effectiveness of a web site. Topics include the Internet, web,mobile apps, content management systems, cloud computing, and others.

MATH 5170 - Discrete Mathematics for Data Analytics - 3 credits

A survey of discrete modeling techniques used in data analysis. Topics covered include: propositional logic; set theory; algorithms; relations; finite state machines; graph theory.

PUBM 5450 - GIS for Public Administrators - 3 credits

The goal of the course is to learn a basic, practical understanding of Geographic Information Systems (GIS) concepts, techniques, and real-world applications. Students will learn how to critically examine spatial data in order to address social and environmental challenges. The course will be lab-based with readings and group discussions to understand real world GIS applications. Elective.