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. Rules that Accelerated Masters students should abide by are listed here. Accelerated Masters students should ensure that they only enroll for graduate versions of courses once they matriculate in the DATS program.)
Students who matriculated in Fall 2020 and earlier can follow the DATS curriculum (entered Fall 2019 or later) OR the most current curriculum below.
1. Foundations (two course units)
- Programming Languages & Techniques (PL): Programming Languages & Techniques (CIT 590) or Introduction to Software Development (CIT 591)
- Linear Algebra (CIS 515) OR Computational Linear Algebra (Math 513)
If students have taken these courses as part of another program, the requirement may be waived.
For Accelerated Masters, the programming requirement can be waived with successful completion of a B+ or higher in CIS 120. The linear algebra requirement can be waived with a B+ or higher in Math 312. The Stats requirement can be waived with a B+ or higher in Stat 431. A student may also waive Foundation requirements with any other relevant course after getting department approval.
Please understand that CIS 120, Math 312, Stat 431 or any other relevant undergraduate course can only be used to waive these requirements and CAN NOT be used as courses to count towards the master’s degree. Upon waiving these requirements, students must take Technical Electives or a course of their choice (subject to department approval) in lieu of them.
2. Core Requirements (three course units)
- Statistics for Data Science (ESE 542)
- 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) or Data-driven Modeling and Probabilistic Scientific Computing (ENM 531) or Data Mining: Learning from Massive Datasets (ESE 545)
3. Technical & Depth Area Electives (five course units)
Students must choose courses from at least 3 of the buckets listed below.
BUCKETS for Technical & Depth Area Electives
Applications
Methods
- Artificial Intelligence (CIS 521)
- Deep Learning for Data Science (CIS 522)
- Computational Linguistics (CIS 530)
- Machine Perception (CIS 580)
- Computer Vision (CIS 581)
- Advanced Topics in ML (CIS 620)
- Advanced Topics in Computer Vision (CIS 680)
- Principles of Deep Learning (ESE 546)
- Learning in Robotics (ESE 650)
- Modern Data Mining (STAT 571)
- Advanced Linear Algebra (AMCS 514)
- Algorithms (CIS 502)
- Computational Learning Theory (CIS 625)
- Randomized Algorithms (CIS 677)
- Algorithms & Computation (CIT 596)
- Numerical Methods (ENM 502)
- Data-driven Modeling and Probabilistic Scientific Computing (ENM531)
- Introduction to Optimization Theory (ESE 504)
- Data Mining: Learning from Massive Datasets (ESE 545)
- Simulation Modeling and Analysis (ESE 503)
- Convex Optimization (ESE 605)
- Information Theory (ESE 674)
- Bayesian Statistical Theory and Methods (STAT 927)
Information re: Data Science (DATS) Minor can be accessed here
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