Data Science (M.S.)

Location(s): Brookings Main Campus

$Two students looking at monitors with data.$

Program Coordinator/Contact

Eun Heui Kim, Department Head
Donald Vestal, Associate Professor/Graduate Coordinator
Department of Mathematics and Statistics
Chicoine Architecture, Mathematics and Engineering Hall 209, Box 2225
605-688-6196

Program Information

The SDSU M.S. 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 both the private and public sectors. It is recommended that this program be started in the summer semester. Failure to start in the summer may increase the amount of time necessary to complete the program.

Course Delivery Format

The six core courses are delivered online. The 4 electives are typically delivered in on campus classrooms, with occasional courses offered online.

Student Learning Outcomes

Communication: Students will understand the foundations of data science, with a specific focus on the interplay between computational complexity and statistical efficiency.
Ethics: Students will understand ethical implications of using data and statistical models for making decisions.
Analysis: Students will perform exploratory data analysis and statistical inference in appropriate application areas.
Application of methods: Students will apply the methods in artificial intelligence, machine learning, or pattern recognition to real data.
Students will proficiently use at least one statistical software among R, SAS, PYTHON, STATA, JMP, or SQL.
Students will appropriately communicate the results of their analysis to various audiences.

Available Options for Graduate Degrees

Master of Science

Non-Thesis

30 Credit Hours

Core Requirements

INFS 762 - Data Warehousing and Data Mining Credits: 3 (DSU)
INFS 772 - Programming for Data Analytics Credits: 3 (DSU)
INFS 774 - Big Data Analytics Credits: 3 (DSU)
STAT 600 - Statistical Programming Credits: 3
STAT 601 - Modern Applied Statistics I Credits: 3
STAT 602 - Modern Applied Statistics II Credits: 3

Electives

Credits: 12

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

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

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

MATH 675 - Operations Research II Credits: 3
STAT 553 - Applied Bayesian Statistics Credits: 3
STAT 651 - Predictive Analytics II Credits: 3
STAT 684 - Statistical Inference I Credits: 3
STAT 685 - Statistical Inference II Credits: 3
STAT 686 - Regression Analysis I Credits: 3
STAT 687 - Regression Analysis II Credits: 3
STAT 715 - Multivariate Analysis I Credits: 3
STAT 721 - Statistical Computing and Simulation Credits: 3
STAT 731 - Survival Analysis Credits: 3
STAT 742 - Spatial Statistics Credits: 3
STAT 792 - Topics (COM) Credits: 1-3 (3 credits required)

Total Required Credits: 30 (Non-Thesis)

Additional Admission Requirements

GRE: Not required.
TOEFL: Program requirement minimum score of 80 internet-based, OR
TOEFL Essentials: Program requirement minimum score of 8.5, OR
IELTS: Program requirement minimum score of 6.5, OR
Duolingo: Program requirement minimum score of 110

Two letters of recommendation with at least one from an individual who can address the applicant’s technical knowledge background and ability to perform research, a research statement or statement of interest area, and transcripts are required.

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.
An undergraduate grade point average of at least 3.0 on 4.0 scale.
Transcript showing completion of courses in key areas equivalent to:
- Database design/programming (SAS, Python, or R) including familiarity with SQL (STAT 410-510 or STAT 415-515 or equivalent)
- Understanding of the principles of programming (CSC 150 or INFO 101 or equivalent)
- Understanding of statistical principles, including multivariate statistical analysis (STAT 441-541 or equivalent)
Undergraduate preparatory courses requiring 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 are:
- MATH 123 Calculus I
- MATH 125 Calculus II
- MATH 215 Matrix Algebra OR MATH 412 Linear Algebra
- STAT 381 Introduction to Probability and Statistics

Accelerated Master’s Program

The accelerated master’s program will be available to eligible SDSU students. Up to twelve (12) graduate level credits may apply to the undergraduate degree as major requirements or electives with approval from the student’s academic advisor, department head/school director, and college dean. Students must follow SDSU Policy 2:22 Use of Graduate Credit for Undergraduate Degree Requirements.

General Requirements

Graduate students should consult with their advisor before registering for graduate coursework. For additional information, refer to the Master’s Degree Requirements .