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Nov 21, 2024
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2022-2023 Graduate Catalog [Archived Catalog]
Data Science (M.S.)
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Program Coordinator/Contact
Kurt D. Cogswell, 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.
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.
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.
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Available Options for Graduate Degrees
| Master of Science |
Option C - Coursework Only |
30 Credit Hours |
The following courses will be the default elective curriculum beyond the core courses in the M.S. in Data Science program.
The following courses are available to students with appropriate mathematical and statistical prerequisite knowledge.
Total Required Credits: 30 (Option C)
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
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 (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)
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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
General Requirements
Graduate students should consult with their advisor before registering for graduate coursework.
For additional information refer to the Master’s Degree Requirements .
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