MS Program

Master of Science (M.S.) in Data Science Program Overview

Welcome to the HDSI MSDS Graduate Program!

The goal of this master’s program is to teach students knowledge and skills required to be successful at performing data driven tasks, and lay the foundation for future researchers who can expand the boundaries of knowledge in Data Science itself. To meet its goals, the Master of Science in Data Science (MSDS) program consists of two components: formal courses, and degree completion choices between a thesis or comprehensive examinations.

The Master of Science in Data Science’s formal courses are structured as a total of twelve (12) 4-unit courses grouped into: Foundational (Group A), Core (Group B), and Elective requirements (Group C). These course requirements are intended to ensure that students are exposed to( A) fundamental concepts and tools (B) advanced, up-to-date views in topics central to Data Science for all students and (C) a deep understanding of current research or applications in Data Science.

Group A: Foundational Courses

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, and optimization. The MS program is designed so that students lacking in any (and/or all) of these foundational knowledge and skills can take and receive credit for a maximum of 16-units from the following five courses: DSC 200, DSC 202, DSC 210, DSC 211 and DSC 212.

Admitted MS Students who have already completed these courses at a graduate level or equivalent may submit a petition to waive these course requirements and use these petition to waive pre-requisite for Group B & C courses.

Group B: Core Courses

Students are required to take a minimum of 6 courses for letter-grade credit from Group B courses. These courses build upon Group A Foundational courses. All students must take these 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 206, DSC 215, DSC 242, DSC 243, DSC 244, DSC 245, DSC 250, DSC 261.

Group C: Elective Courses

Students can take advantage of electives to complete their course of study. These courses can be advanced courses in 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.

Successful completion of the program requires completion of a Thesis (Plan I) or a course-based Comprehensive Examination (Plan II) 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 focus such as economics, biology, medicine, etc upon approval of the student’s faculty advisor.

MS Plan I: Thesis Option

Students who choose the MS Plan 1: Thesis Option must sign up for a minimum of 8 and a maximum of 12 units of DSC 299 (Thesis Research) which can also be used to meet Group C requirements. The student will perform thesis research under the guidance of a thesis advisor and a thesis committee consisting of at least three members.

The Master thesis produced by the student must be approved by their thesis committee. It is required that at least two members of the committee are members of the HDSI faculty council; one of the three committee members can be an HDSI industry fellow with an adjunct appointment or a faculty member drawn from another department or division. Alternatively, the industry fellow may be requested to serve as the fourth member of the committee. The committee must be approved by the Graduate Division by the end of the fourth quarter in the MS program.

MS Plan II: Comprehensive Exam Option

The Comprehensive Examination option is designed to evaluate a student’s ability to apply their Data Science knowledge to solve problems and demonstrate aptitude in three different course-hosted subject areas: Machine Learning/Computing, Math/Statistics, and Systems/Algorithms.

In order to receive credit for a comprehensive exam, students must also receive a passing grade (B- or higher) in the course. Students are required to successfully pass the comprehensive examination in three courses drawn from each of the three areas. Students are permitted up to five attempts, that is, five different courses. No more than three course-hosted comprehensive examinations can be taken in a single quarter, and no comprehensive examination can be repeated in a single quarter. The courses marked for comprehensive examination must be taken for a letter grade. While some courses may be used towards multiple areas, students cannot count the same course towards more than one comprehensive exam area requirement.

The comprehensive examination is integrated into the host courses, and in most cases, the associated work serves dual purposes, contributing independently to the student’s course grade and comprehensive examination score. The comprehensive examination typically consists of a specific class assignment or examination, or a portion thereof, that has been explicitly approved by the MS program committee.

Course-hosted examinations are registered at the beginning of each quarter and students must register in advance by the specified deadline for the examination. The examination is supervised by a faculty committee responsible for the content, evaluation and administration of the examination which is separate from the course instructor who is responsible for the course grade but not success in the comprehensive examination.

Machine Learning/ Computing	Math/Statistics	Systems/Algorithms
DSC 240	DSC 241	DSC 204A
DSC 243	DSC 243	DSC 206
	DSC 245	DSC 245

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