MS Data Science

Admission Requirements

Candidates must meet all of the University admission requirements. Students can be admitted in either classified or conditionally classified standing.

To be admitted to classified standing, applicants must have earned

  • A Bachelor's degree in the sciences or engineering from a regionally accredited institution with a minimum GPA of 3.0 (based on a 4.0 scale), and have completed of all program prerequisites listed below.
  • Two to three letters of recommendation are also required.
  • The GRE General Test score must also be submitted along with the packet. There is no minimum requirement, but the GRE score will be used to compare and rank applicants. GRE is waived for Spring 2021 admissions.

To enter this program with classified standing, a student must have passed the following prerequisites courses (with the listed required grades in some cases):

  1. A multivariable calculus course (e.g., Math 32 at SJSU), with a grade of B or better
  2. A linear algebra course (e.g., Math 129A at SJSU), with a grade of B or better
  3. A calculus-based upper division statistics course (e.g., Math 161A at SJSU), with a grade of B or better
  4. A data structures course (e.g., CS 146 at SJSU), with a grade of B or better
  5. A probability theory course (e.g., Math 163 at SJSU)
  6. An advanced course in object-oriented programming (e.g., CS 151 or CMPE 135 at SJSU) or two semesters of statistical programming coursework, such as the following at SJSU:
    a) Math 167R Statistical Programming with R, and
    b) Math 167PS Introduction to Python Programming and SQL

Applicants from countries in which the native language is not English must submit TOEFL scores. Minimum TOEFL scores acceptable for admission are 600 (Paper- based), 250 (Computer-based),or 100 (Internet-based).

List of Required Courses

Catalog #



CS 156

Introduction to Artificial Intelligence


CS 157A

Introduction to Database Management Systems


CS 200W

Graduate Technical Writing


CS 274

Topics in Web Intelligence


Math 164

Mathematical Statistics


Math 261A

Regression Theory and Methods


Math 253

Mathematical Methods for Data Visualization


Math 252

Cluster Analysis


Math 251 or
CS 271

Statistical and Machine Learning Classification or
Topics in Machine Learning



(subject to approval by program coordinator)


Math 297A or
CS 297

Preparation for Writing Project, Research Project or Thesis Preparation for Writing Project or Thesis


Math/CS 298 or Math/CS 299

Master's Writing Project or
Master’s Thesis


Students who enter the program having already completed courses equivalent in level and content to any of those required for the degree may be allowed to substitute an appropriate alternative course upon advanced approval by the MS Data Science Coordinator.

The maximum number of upper-division undergraduate units that can be applied toward the master's degree is 9.

The elective can be selected from the list of courses offered in the Math/Stats and CS departments and requires approval from the program coordinator.

Total units required to complete the Degree: 36 units