Program Requirements

Students need to complete 30 course credits to graduate. Among the 10 courses, eight are required courses and two are electives.

The curriculum includes:

  1. Data Science Foundation (6)
    1. Probability and Statistical Inference for Data Science (3) 16:954:581 [Fall]
    2. Regression and Time Series Analysis for Data Science (3) 16:960:596 [Fall]
    3. Data Wrangling and Husbandry (3) 16:954:597 [Spring]
  2. Computer Science Foundation (6)
    1. Data Structures and Algorithms (3) 16:198:512 (CS) [Fall]
    2. Database (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 and Machine Learning Methods (3) 16:958:588 [Spring]
    3. Statistical Learning for Data Science (3) 16:954:534 [Fall]
  4. Basic Electives (choose two)
    1. Time Series, Forecasting and Advanced Analytics (3) 16:960:565 [Spring]
    2. Convex Optimization for Engineering Applications (3) 16:332:509 (ECE) or (Expended) Linear Programming (3) 16:198:521 (CS) []
    3. Data Visualization (3) 16:332:562 (ECE) [] or Visual Analytics 16:198:67x (CS) []
    4. Bayesian Analysis (3) 16:960:688 (Stat) [Spring]
    5. Advanced Analytics using Statistical Software (3) 16:954:577 [Fall]
    6. Capstone Project (3) [Fall]
  5. Other Advanced Topics (need to be approved by Director)
    1. Independent Study (3) 16:954:683
    2. Mathematical Analysis (3) 16:640:411 (Math)
    3. Theory of Probability (3) 16:960:592
    4. Theory of Statistics (3) 16:960:593
    5. Advanced Database Management (3) 16:198:541 (CS)
    6. Functional Data Analysis (3)
    7. Survey Sampling (3) 16:960:576
    8. Advanced Design of Experiments (3) 16:960:591
    9. Introduction to Parallel Computing and Distributed Computing (3) 16:332:566 (ECE)
    10. Analysis of Network and Media Data (3)
    11. Biostatistics (3) 16:960:584 (3)
    12. Biostatistics II (3) 16:960:585 (3)
  6. Practical Training
    1. Practical Training(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-C) are intended for students who wish to continue to the PhD program.

 

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)