Aug 11, 2020
Kurt D. Cogswell, Department Head
Rong Fan, Statistics Lecturer/MS Data Science Coordinator
Department of Mathematics and Statistics
Architecture, Mathematics and Engineering 209, Box 2225
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 today’s professional world, both private and public sector.
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. (Communication Skills; Transferable Skill: Ethics - Moral Decision Making/Moral Reasoning)
- Ethics: Students will understand ethical implications of using data and statistical models for making decisions. (Communication Skills; Transferable Skill: Ethics - Moral Decision Making/Moral Reasoning)
- Analysis: Students will perform exploratory data analysis and statistical inference in appropriate application areas. (Communication Skills; Transferable Skill: Ethics - Moral Decision Making/Moral Reasoning)
- Application of methods: Students will apply the methods in artificial intelligence, machine learning, or pattern recognition to real data. (Communication Skills; Transferable Skill: Ethics - Moral Decision Making/Moral Reasoning)
- 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
Courses will typically be delivered in on-campus classrooms, with occasional courses offered online.
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 575paper-based, 90-91 internet-based, OR
IELTS: Program requirement minimum score of 6.0
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)
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.
Graduate students should consult with their advisor before registering for graduate coursework.
For additional information refer to the Master’s Degree Requirements .