Program Requirements

Students need to complete 30 course credits to graduate. Among the 10 courses, eight are required courses and two are electives. 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) 16:960:596 [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)

 

Contacts

General & Admission Inquiries: msds@stat.rutgers.edu

Co-Director & Academic Advisor:
Dr. Cun-Hui Zhang
czhang@stat.rutgers.edu

Co-Director & Academic Advisor:
Dr. Sijian Wang
sijian.wang@stat.rutgers.edu

Corporate Relations:
Mohannad Aama
mohannad.aama@rutgers.edu