There are Introductory, Core, and Elective and Research requirements (Group A, B, and C courses below) for the masters program. These course requirements are intended to ensure that students are exposed to (1) fundamental concepts and tools (Foundation), (2) advanced, up-to-date views in topics central to Data Science for all students (the Core requirement), and (3) a deep, current view of their research or application are (the Elective requirement). Courses may not fulfill more than one requirement.

The master of science in data science program is structured as a total of twelve (12) 4-unit courses grouped into foundational, core and specialization areas as described below. Successful completion of the program requires completion of a Thesis or a course-based comprehensive examination that tests integrative knowledge across multiple courses. Out of the 48 units, at least 40 units must be using graduate-level courses. In addition, 2 out of 10 graduate courses can be in areas not directly related to data science but a domain specialization such as economics, biology, medicine etc upon approval of the student’s faculty advisor.


Group A: Introductory courses: maximum of four course credit

These courses seek to provide five critical foundational knowledge and skills that each student graduating from the master’s program is expected to receive at a graduate level: programming skills, data organization and methods skills, numerical linear algebra, multivariate calculus, probability and statistics.

The program is designed so that students lacking in any (and all) of these foundational knowledge and skills can take credit for a maximum of four courses from the following five courses: DSC 200, DSC 202, DSC 210, DSC 211 and DSC 212.


Group B: Core Courses: 3 required courses, minimum of six courses

These courses build upon foundational courses. All students must take three required core courses: DSC 240, DSC 241 (*), and DSC 260. In addition, students can select at least three out of the following core courses: DSC 203, DSC 204A (*), DSC 204B, DSC 242, DSC 243, DSC 244, DSC 245, DSC 250, DSC 261.

(*) 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.


Group C: Elective and Specialization Courses: remaining course credit requirements

The MS students can take advantage of electives to complete their course of study. These courses can be advanced courses in core Data Science subjects listed under Group B as research topics (DSC 291) courses, or they can be graduate (or upper-division undergraduate) courses in other departments subject to approval by the student’s HDSI faculty advisor.

As a matter of guidance, students can choose from the following elective or specialization tracks to complete course requirements.


General Elective Courses:

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.


Specialization Areas: minimum of 3 courses required

Upon prior approval from a graduate advisor, students can sign up for an available specialization area for an “Master of Science in Data Science with specialization in specialization-area” degree. A specialization requires a minimum of three courses in a specialization area.


Specialization: Bioengineering

BENG 218, BENG 203, BENG 211, BENG 213, BENG 221, BENG 230A-B, BENG 276, COGS 278, PHYS 278, FMPH 223, FMPH 226


Specialization: Business (Marketing)

MGT 475, MGT 477, MGT 489, MGTA 455, MGTA 479


Specialization: Business (Supply Chain and Technology)

MGT 450, MGT 451, MGTA 456, MGTA 463


Specialization: Business (Finance)

MGT 407, MGTF 402, MGTF 404, MGTF 405, MGTF 406, MGTF 415


Specialization: Machine Vision and Interaction Design

COGS 202, COGS 220, COGS 225, COGS 283


Specialization: Computational Neuroscience

COGS 260, BGGN 246, BGGN 260, COGS 260 (or NEU 282), COGS 280


Specialization: Networks

MATH 261A, MATH 277A, MATH 289A, MATH 289B, DSC 205, BNFO 286, POLI 287, SIOB 276, ECE 227, MAE 247


Availability of all specializations is not guaranteed. Additional specialization areas may be added by student petition.