Program Requirements

Students need to complete 30-course credits to graduate. Among the 10 courses, eight are required courses and two are electives. At most two course grade points are allowed to be "C+" or lower. In addition to coursework, the degree requirements include completion of a master’s research project. Although the master’s research project can be based on a course project completed by the student, it must contain enhancements or extensions agreed with a faculty member who is the Project Supervisor. 

The curriculum includes:

1. Data Science Foundation (9)
   1. Probability and Statistical Inference for Data Science (3) 16:954:581 [Fall]
   2. Regression and Time Series Analysis for Data Science (3) 16:954:596 [Fall]
   3. Data Wrangling and Husbandry (3) 16:954:597 [Spring]
2. Computer Science Foundation (6)
   1. Introduction to Data Structures and Algorithms (3) 16:198:512 (CS) [Fall]
   2. Database Management Systems (3) 16:198:539 (CS) [Spring]
3. Required Analytics and Learning (9)
   1. Statistical Models and Computing (3) 16:954:567 [Spring]
   2. Financial Data Mining (3) 16:958:588 (FSRM) [Spring]
   3. Statistical Learning for Data Science (3) 16:954:534 [Fall]
4. Basic Electives (choose two)
   1. Advanced Analytics using Statistical Software (3) 16:954:577 [Fall]
   2. Life Data Analysis (3) 16:960:542 (STAT) [Spring] 
   3. Categorical Data Analysis (3) 16:960:553 (STAT) [Fall]
   4. Applied Time Series Analysis (3) 16:960:565 (STAT) [Spring]
   5. Applied Multivariate Analysis (3) 16:960:567 (STAT) [Spring]
   6. Bayesian Data Analysis (3) 16:960:568 (STAT) [Spring]
   7. Convex Optimization for Engineering Applications (3) 16:332:509 (ECE) [Spring] or 
      Linear Programming (3) 16:198:521 (CS) [Fall]
   8. Data Interaction and Visual Analytics 16:198:526 (CS) [Spring]
   9. Capstone Project (3) [Fall]
5. Other Advanced Topics (need to be approved by Director)
   1. Independent Study in the Application of Data Science(3) 16:954:683 [Spring & Fall]
   2. Theory of Functions of Real Variable I (3) 16:640:501 (MATH) [Fall] and
      Theory of Functions of Real Variable II (3) 16:640:502 (MATH) [Spring]
   3. Theory of Probability (3) 16:960:592 (STAT) [Fall]
   4. Theory of Statistics (3) 16:960:593 (STAT) [Fall]
   5. Intermediate Statistical Methods (3) 16:960:596 (STAT) [Fall]
   6. Advanced Applied Statistics for Data Scientists II (3) 16:960:597 (STAT) [Spring]
   7. Stochastics Process (3) 16:960:654 (STAT) [Spring]
   8. Biostatistics I (3) 16:960:584 (STAT) [Fall] and
      Biostatistics II (3) 16:960:585 (STAT) [Spring]
   9. Design of Experiments (3) 16:960:590 (STAT) [Fall]
   10. Advanced Database Management (3) 16:198:541 (CS) [Spring]
   11. Introduction to Parallel Computing and Distributed Computing (3) 16:332:566 (ECE) [Fall]
6. Practical Training
   1. Practical Training in Statistics for Data Science (0) 16:954:690

Note 1. For advanced students, courses in (I) and (II) can be waived and replaced with more advanced electives.

Note 2. Courses listed in (V)(A-G) are intended for students who wish to continue to the PhD program.

Example Course Schedule - Full Time

Example Course Schedule - Part Time