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.