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 Analysis (3) 16:958:563 (FSRM) [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. Special Topics: Database Management for Advanced Science Applications (3) 16:954:694 [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. Advanced Methods/Statistics for Risk Management Practice 16:958:534 (FSRM) [Spring]
    3. Advanced Statistical Methods in Finance 16:958:535 (FSRM) [Spring]
    4. Advanced Risk Evaluation and Management 16:958:536 (FSRM) [Fall]
    5. Financial Time Series Analysis (3) 16:958:565 (FSRM) or Applied Time Series Analysis (3) 16:960:565 (STAT) [Spring]
    6. Advanced Simulation Methods for Finance 16:958:587 (FSRM) [Fall]
    7. Advanced Programming for Financial Statistics & Risk Management 16:958:589 (FSRM) [Spring]
    8. Foundations of Financial Statistics & Risk Management 16:958:590 (FSRM) [Fall]
    9. Algorithm Trading & Portfolio Management 16:958:591 (FSRM) [Fall]
    10. Life Data Analysis (3) 16:960:542 (STAT) [Spring] 
    11. Categorical Data Analysis (3) 16:960:553 (STAT) [Fall]
    12. Applied Multivariate Analysis (3) 16:960:567 (STAT) [Spring]
    13. Bayesian Data Analysis (3) 16:960:568 (STAT) [Spring]
    14. Introduction to Deep Learning (3) 16:332:530 (ECE) [Spring]
    15. Introduction to Parallel Computing and Distributed Computing (3) 16:332:566 (ECE) [Fall]
    16. Convex Optimization for Engineering Applications (3) 16:332:509 (ECE) [Spring] or 
      Linear Programming (3) 16:198:521 (CS) [Fall]
    17. Data Interaction and Visual Analytics 16:198:526 (CS) [Spring]
    18. Advanced Database Management (3) 16:198:541 (CS) [Spring]
    19. Capstone Project (3) [Final Semester]
  5. Other Topics
    1. Courses subject to approval by MSDS Program Director
      Introduction to Methods and Theory of Probability (3) 16:960:582 (STAT) [Fall]
      Methods of Statistical Inference (3) 16:960:583 (STAT) [Spring]
      Biostatistics I (3) 16:960:584 (STAT) [Fall]
      Biostatistics II (3) 16:960:585 (STAT) [Spring]
      Design of Experiments (3) 16:960:590 (STAT) [Fall]
      Independent Study in the Application of Data Science(3) 16:954:683 [Spring & Fall]
    2. Advanced courses subject to approval by Statistics Graduate Program Director
      Theory of Probability (3) 16:960:592 (STAT) [Fall]
      Theory of Statistics (3) 16:960:593 (STAT) [Fall]
      Intermediate Statistical Methods (3) 16:960:596 (STAT) [Fall]
      Advanced Applied Statistics for Data Scientists II (3) 16:960:597 (STAT) [Spring]
      Stochastics Process (3) 16:960:654 (STAT) [Spring]
    3. Advanced courses subject to approval by Mathematics Graduate Program Director
      Theory of Functions of Real Variable I (3) 16:640:501 (MATH) [Fall]
      Theory of Functions of Real Variable II (3) 16:640:502 (MATH) [Spring]
  6. Practical Training
    1. Practical Training in Statistics for Data Science (0) 16:954:690

Note 1. Courses listed in (V)(B-C) are intended for students who wish to continue to the PhD program

Note 2. Courses in (I), (II) and (III) can be waived and replaced with the following electives
                960:592 (F) and 960:593 (F) fulfill 954:581 and one elective
                960:596 (F) and 960:565 (S) fulfill 954:596 and one elective
                960:582 (F) and 960:583 (F) fulfill 954:581 and one elective

Example Course Schedule - Full Time

 

Fall Spring Fall
16:954:581 16:198:539 16:958:534
16:960:596 16:954:567 Elective
16:198:512 16:954:588  
Elective 16:954:597  

 

Example Course Schedule - Part Time

Fall  16:954:581, 16:960:596
Spring  16:954:567, 16:954:588
Summer   Data Structures and Algorithms, or Database
Fall  16:958:534, 16:198:512, or 16:954:577
Spring  16:954:597, 16:198:539 or Elective
Summer   Elective
Fall   Elective (If needed)