Program of Study

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The ten course units for the Data Science degree are divided into three categories:

(As long as the prerequisites for the courses are met, students can complete these courses in any sequence) 

1.  Foundations (two course units)

  • Probability: Intro to Probability & Statistics (ENM 503) or Probability (STAT 510) or Advanced Probability (MATH 546)
  • Programming Languages & Techniques (PL): Programming Languages & Techniques (CIT 590) or Introduction to  Software Development (CIT 591)

For general applicants, if students have taken these courses as part of another program, this may be waived. For submatriculants, the probability requirement can be waived with successful completion of ESE 301 or STAT 430; the programming requirement can be waived with successful completion of CIS 120.

In lieu of these courses, students may take Technical Electives, and are encouraged (but not required) to take a course from Bucket C in lieu of Probability, and a course from Bucket B in lieu of PL.

2. Core Requirements (three course units)

  • Mathematical Foundations: Mathematical Statistics (STAT 512) or Linear Algebra/Optimization (CIS 515)  or Computational Learning Theory (CIS 625)
  • Big Data Analytics: Big Data Analytics (CIS 545)
  • Mining and Learning: Intro to Machine Learning (CIS 519) or Machine Learning (CIS 520) or Modern Data Mining (STAT 571)

3. Technical & Depth Area Electives (five course units)

Students must choose courses from 3 different buckets, one bucket of which can be a 2 semester sequence of thesis/practicum. Two of the courses must represent a depth sequence, which could be the thesis/practicum or (for bucket options B-H) two courses, one of which builds on the other (e.g. is a prerequisite).

BUCKETS for Technical & Depth Area Electives

Register for two credits of DATS 597/Master’s Thesis or two credits of DATS 599/Master’s Independent Study. Suggestions for projects will be provided to students. Students may choose from these suggested projects or may also come up with their own project/advisor ideas. Students will be mentored jointly by the Program Director and by an advisor in the area of the project, and must receive approval by Faculty Director.
• Software Systems (CIS 505)
• Software Engineering (CIS 573)
• Computer Systems Programming (CIT 595)
• Advanced Programming (CIS 552)
• Internet and Web Systems (CIS 555)
• Programming and Problem Solving (CIS 559)
Numerical Methods (ENM 502)
Linear Algebra/Optimization (CIS 515)
Complex Analysis (AMCS 510)
Introduction to Optimization Theory (ESE 504)
Regression Analysis (STAT 621)
Stochastic Processes (STAT 533)
Bayesian Methods (STAT 542 )
Convex Optimization (ESE 605)
• Information Theory (ESE 674)
• Databases (CIS 550)
Sample Survey Methods (STAT 920)
Observational Studies (STAT 921)
• Computational Linguistics (CIS 530)
• Computer Vision (CIS 580, CIS 581)
• Advanced Topics in Computer Vision (CIS 680)
• Computational Learning Theory (CIS 625)
• Data Mining: Learning from Massive Datasets (ESE 545)
• Modern Data Mining (STAT 571)
• Advanced Topics in ML (CIS 700)
• Forecasting and Time-Series Analysis (STAT 910)
• AIgorithms (CIS 502, CIS 677, CIT 596)
• AI (CIS 521)
• Learning in Robotics (ESE 650)
• Modern Regression for the Social, Behavioral and Biological Science (STAT 974)
• Simulation Modeling and Analysis (ESE 603)
• Control of Systems (ESE 505)
• Topics In Computational Science and Engineering (ENM 540)
• Brain-Computer Interfaces (BE 521)
• Network Neuroscience (BE 566)
• Modeling Biological Systems (BE 567)
• Bioinformatics (STAT 953)
• Computational Neuroscience (PHYS 615)
• Econometrics (ECON 705, ECON 706, ECON 721, ECON 722)
Applied Probability Models in Marketing (MKTG 476/776)

Information re: Data Science (DATS) Minor can be accessed here


More Information

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