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. Introduction to Parallel Computing and Distributed Computing (3) 16:332:566 (ECE) [Fall]
   8. Convex Optimization for Engineering Applications (3) 16:332:509 (ECE) [Spring] or 
      Linear Programming (3) 16:198:521 (CS) [Fall]
   9. Data Interaction and Visual Analytics 16:198:526 (CS) [Spring]
   10. Advanced Database Management (3) 16:198:541 (CS) [Spring]
   11. Capstone Project (3) [Final Semester]
5. Other Topics
   1. Courses subject to approval by MSDS Program Director
      Introduction to Methods and Theory of Probablility (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

Example Course Schedule - Part Time