Data Science (M.S.)

Print Degree Planner (opens a new window) |

Program Information
The SDSU MS in Data Science is a one-year program that provides graduates with the statistical, mathematical, and computational skills needed to meet the large-scale data science challenges of today’s professional world. The curriculum incorporates current techniques in statistics, operations research, predictive modeling, data mining, forecasting, big data programming and management, and data visualization. The program’s focus is on application and interpretation of modern data analysis techniques of known value in today’s professional world, both private and public sector.

Available Options for Graduate Degrees

Master of Science Option C

Core Requirements

Required: 18

INFS 762 - Data Warehousing and Data Mining (DSU) Credits: 3
INFS 772 - Programming for Data Analytics (DSU) Credits: 3
INFS 774 - Big Data Analytics (DSU) Credits: 3
STAT 700 - Statistical Programming Credits: 3
STAT 701 - Modern Applied Statistics I Credits: 3
STAT 702 - Modern Applied Statistics II Credits: 3

Electives: 12

The following courses will be the default elective curriculum beyond the core courses in the MS in Data Science program.

MATH 575 - Operations Research Credits: 3
STAT 545 - Nonparametric Statistics Credits: 3
STAT 551 - Predictive Analytics I Credits: 3
STAT 560 - Time Series Analysis Credits: 3

The following courses are available to students with appropriate mathematical and statistical prerequisite knowledge.

MATH 541 - Applied Probability Theory Credits: 3
MATH 775 - Operations Research II Credits: 3
STAT 715 - Multivariate Analysis I Credits: 3
STAT 721 - Statistic Computing/Simulation Credits: 3
STAT 742 - Spatial Statistics Credits: 3
STAT 751 - Predictive Analytics II Credits: 3
STAT 784 - Statistical Inference I Credits: 3
STAT 785 - Statistical Inference II Credits: 3
STAT 786 - Regression Analysis I Credits: 3
STAT 787 - Regression Analysis II Credits: 3
STAT 792 - Topics Credits: 3

Total Credits: 30

Additional Admission Requirements

GRE: Not required.
TOEFL: Program requirement minimum score of 550 paper-based, 79 internet-based, 213 computer-based OR
IELTS: Program requirement minimum score of 6.5

In addition to meeting Graduate School admission requirements, applicants for graduate study for the M.S. in Data Science must have:

Baccalaureate degree from an institution of higher education with full regional accreditation for that degree.
The applicant must have an undergraduate grade point average of at least 3.0 on 4.0 scale.
Transcript should show completion of courses in key areas equivalent to:
- Database design/programming including familiarity with SQL (STAT 410/510 or equivalent)
- Understanding of the principles of programming (CSC 150 or INFO 101 or equivalent)
- Understanding of statistical principles (STAT 441/541 or equivalent)
Students may be required to take undergraduate or foundation classes in order to make up for deficiencies.
Undergraduate preparatory courses required of entering students include two semesters of calculus, one course in matrix or linear algebra, one introductory course in calculus-based probability and statistics. SDSU courses that would satisfy these requirements would be:
- MATH 123 Calculus I
- MATH 125 Calculus II
- MATH 215 Matrix Algebra OR MATH 315 Linear Algebra
- STAT 381 Introduction to Probability and Statistics
Students with other educational backgrounds may be admitted conditionally. They will be required to complete the necessary coursework to eliminate deficiencies in their background during their first semester in the program.

General Requirements (Master’s Degree)

Graduate students should consult with their advisor before registering for graduate work.

See Master’s Degree Requirements .