The Master of Science in Data Science program, offered by the College of Science’s Department of Computer Science and Department of Mathematics and Statistics, provides students with a Bachelor’s degree in the sciences or engineering with foundational knowledge, experience, and skills that are crucial for a data science career. It features a multidisciplinary curriculum in applied math, statistics, computer science, and their intersection - machine learning. Its rigorous, balanced coursework provides students with strong mathematical, computing, and data skills, as well as project experience. Graduates of this program will have the necessary theoretical knowledge, practical data analysis and coding skills for solving complex real-world problems based on massive amounts of data and also meet the qualifications for various data scientist positions in industry and government.
Program Learning Outcomes (PLOs)
Students will be able to:
- Apply computer science knowledge and tools to assist in performing data science tasks.
- Summarize and evaluate statistical and machine learning concepts, models, and techniques.
- Integrate multidisciplinary knowledge and software to tackle challenging, complex data science tasks.
- Communicate effectively, both orally and in writing, data science concepts, algorithms, and results to a broad audience.
- Identify ways in which data scientists can contribute to the cultural and economic well-being of diverse societies in local, national, and global scopes.
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 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. To enter this program with classified standing, a student must have passed the following prerequisites courses (with the listed required grades in some cases):
- A multivariable calculus course (e.g., MATH 32 at SJSU), with a grade of B or better.
- A linear algebra course (e.g., MATH 39 at SJSU), with a grade of B or better.
- A calculus-based upper-division statistics course (e.g., MATH 161A at SJSU), with a grade of B or better.
- A data structures and algorithms course (e.g., CS 146 at SJSU), with a grade of B or better.
- A probability theory course (e.g., MATH 163 at SJSU).
- 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:
- MATH 167R - Statistical Programming with R
- 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).
Advancement to Candidacy
Students may advance to candidacy after completing all admission conditions and all prerequisites for the culminating experience including the Graduation Writing Assessment Requirement (GWAR).
Requirements for Graduation
University Graduation Requirements
Students must complete all residency, curriculum, unit, GPA, and culminating experience requirements as outlined in the Graduation Requirements section of the Graduate Policies and Procedures.
Master’s Requirements (36 units)
Culminating Experience (6 units)
Both plans require two semesters of enrollment in 6-unit coursework in order to conduct the proposed project or thesis study. The student is responsible for securing the commitment of a full-time tenured or tenure-track faculty member who agrees to serve as his or her advisor. The student must also secure the commitments of two additional members, one of whom must be a full-time tenured or tenure-track faculty member, to serve as the student’s committee. Topics and committee selections must be approved by the MS Data Science Coordinator. Either plan requires writing a manuscript in a formal format describing original data science research, which is submitted for review by the student’s committee. In addition, the student must successfully pass a comprehensive oral examination by the student’s committee based on the conducted project or thesis study.
Total Units Required (36 units)
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.