Flexible Online and On-Campus Learning Options
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