The M.S. degree requires 30 credit hours, consisting of the 4 core courses, 5 elective courses providing a deeper understanding of specific methods, tools, and specific areas of application, and a 3-credit capstone practicum selected from an approved set of challenge areas developed in partnership with industry, government, and civic organizations.
Core requirements
Introduction to Data Science and Python
Applied Statistics and Data Analysis
Introduction to Machine Learning
Applications of Data Analytics and Development
Data Analysis Electives
Artificial Intelligence
Practices for Big Data
High-Performance Parallel Computing
Time Series Analysis
Cloud Computing
Pattern Recognition
Predictive Modeling
Visualization Tools
Information Retrieval and Analysis
Internet of Things
Distributed Computing
Data Mining
Data Ethics
Others
Application Electives:
Business Data Analytics
Healthcare Data Analytics
Data Analysis for Security
Government Data and Analysis
Transportation Informatics
Climate and Ecosystem Monitoring
Others
Data Science and Analytics Practicum
CUA and its industry partners are establishing focus areas that students will address in the practicum. The areas are selected to highlight the power of data and analysis in their solutions. In their final semester, students select one of the issue areas and a method of execution. Students currently working can choose issue areas related to the established set, but which are tailored to their work environment and use data sets supplied by their employer. Students will work throughout the semester in a professional project-like manner. They will submit project proposals, plan of action milestone charts, and time lines as part of the practicum. Students will have scheduled reviews at various points that will be held in conjunction with the industry partners. At the end of the semester, each student will give a 30-minute presentation on their project to a panel made up of CUA faculty and industry partners. Depending on the scope of the project, teams can be formed to address multiple aspects of the available data.
