The Master's degree program requires the completion of 30 credits of coursework:
- 12 credits from four required courses (see below)
- 3 credits from either the Master's thesis (56:219:701) or Master's Capstone Project (56:219:603)
- At most 3 credits from the Data Science Internship (56:219:600)
- At most 3 credits from an Independent Study (56:219:601)
- Remaining credits from elective courses from three areas (see below). At least two of the areas must be covered in the electives.
Required Courses
- 56:219:500 Fundamentals of Data Science: Programming and Reasoning
- 56:219:511 Statistical Methods for Data Science
- 56:219:521 Data Visualization
- 56:219:531 Applied Data Mining and Machine Learning
Elective Courses
Courses not listed here may be approved as electives at the discretion of the Graduate Program Director.
Mathematics and Statistics Electives
- 56:219:512 Probability and Stochastic Processes for Data Science
- 56:219:513 Regression and Time Series Forecasting
- 56:645:549-550 Linear Algebra and Applications
- 56:645:563 Statistical Reasoning
Computer Science Electives
- 56:219:501 Algorithmic Problem Solving in Data Science
- 56:219:567 Applied Probability
- 56:219:593 Special Topics
- 56:198:514 Artificial Intelligence
- 56:198:523 Distributed and Cloud Computing
- 56:198:554 Machine Learning
- 56:198:562 Big Data Algorithms
Social Sciences/Humanities/Business Electives
- 56:219:522 Data Management
- 56:219:523 Geographical Information Systems
- 56:202:600 Research Methods in Criminal Justice
- 56:202:601 Data Analysis in Criminal Justice
- 56:209:530 Creative Coding
- 56:209:520 Experimental Emerging Media
- 53:716:502 Business Analytics
- 53:716:535 Big Data Analytics
- 53:716:545 Machine Learning Applications in Business
At most two elective courses can be taken outside the Graduate School. In such cases, course approval and/or special permission will be required from the external school, e.g., the Camden Business School (school code 53).
Graduate School-Camden 
