Detailed Data Science Courses for PhD Program:

Group A courses are introductory level graduate courses in the foundational areas of data science. Group B are core graduate level courses with prerequisites from Group A courses. Group C are advanced, specialized and free-standing courses, often part of the required courses in the Data Science specialization of the Graduate Program in other departments. In all three groups, required courses are indicated as such; they can not be substituted by other courses without exception approval from the graduate program committee.

Group A: Preparatory Courses

There are five important knowledge and skills necessary for understanding (and advancing) core data science. It is, therefore, important that all our entering students either have background preparation or have courses available in the program to ensure a successful completion of the stipulated doctoral degree program. A student can receive credit towards the Ph.D. degree for a maximum of three courses from the list of courses below:

  1. DSC 200: Data Science Programming.

  2. DSC 202: Data Management for Data Science

  3. DSC 210: Numerical Linear Algebra

  4. DSC 211: Introduction to Optimization

  5. DSC 212: Probability and Statistics for Data Science

Group B: Core Courses

Four core courses are required for all Ph.D. students, including those with a Bachelors in Data Science. The four required courses are:

  1. DSC 240: Machine Learning

  2. DSC 260: Data Ethics and Fairness

  3. (*)DSC 241: Statistical Models (or MATH 282B)

  4. (*)DSC 204A: Scalable Data Systems (or CSE 202)

(*) Depending on academic preparation, a Ph.D. student can take an advanced course on Applied Statistics, such as MATH 282B instead of DSC 241. Similarly, instead of DSC204A, a student can take a course on Algorithms, such as CSE 202: Design and Analysis of Algorithms.

In addition, a doctoral student must select at least 2 out of the following 8 core courses

  1. DSC 203: Data Visualization and Scalable Visual Analytics

  2. DSC 204B: Big Data Analytics and Applications

  3. DSC 242: High-dimensional Probability and Statistics

  4. DSC 243: Advanced Optimization

  5. DSC 244: Large-Scale Statistical Analysis

  6. DSC 245: Introduction to Causal Inference

  7. DSC 250: Advanced Data Mining

  8. DSC 261: Responsible Data Science

Thus, doctoral students are required to take a minimum of 6 courses for letter-grade credit from Group B courses. Students can take more than 6 courses from this group to satisfy letter grade course requirements except (satisfactory completion of professional preparation) teaching, survival skills and research seminar courses. Students who satisfy all letter-grade course requirements are expected to enroll into individual research (DSC 298) in a section offered by the faculty advisor to meet residency requirements and maintain graduate student standing during the period of dissertation research.

Group C: Professional Preparation and Elective Courses

Group C courses aim to provide either practical experiences in chosen specialization areas, or advanced training for students preparing for doctoral programs. The courses include required professional preparation courses: 2 unit TA/tutor training (DSC 599), 1 unit of academic survival skills (DSC 295) and 1 unit faculty research seminar (DSC 293), all of which must be completed with a Satisfactory (S) grade using the S/U option.

Professional Preparation Courses

  1. DSC 599: TA/Tutor Training

  2. DSC 293: Faculty Research Seminar

  3. DSC 294: Research Rotation

  4. DSC 295: Academia Survival Skills

General elective and Specialization Courses

Courses here aim to provide advanced training for students in the doctoral programs, or practical experiences in chosen specialization areas. Students can choose from the following elective or specialization tracks. Additional elective courses will be offered based on faculty interest and availability.

DSC 205, DSC 231, DSC 251, DSC 252, DSC 253, DSC 254, DSC 213, DSC 214.

CSE 234, MATH 181 A-B-C, MATH 284, MATH 285, MATH 287A-B, COGS 243.