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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. Statistical Learning for Data Science (3) 16:954:534[Spring]
        3. Advanced Data Mining and Machine Learning Methods (3) 16:958:588 [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

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

Contact Us

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Piscataway, NJ 08854
Phone: 848-445-2690

General Inquiries: office@stat.rutgers.edu
Special Permission: mcollins@stat.rutgers.edu