The purpose of the program is to deliver a comprehensive curriculum in the fields of statistics and data science to prepare students with diverse backgrounds (including statistics, mathematics, computer science, engineering, and other quantitative fields) for the data science workforce or a doctoral program. The program curriculum emphasizes the following aspects. First, students will be trained in-depth in modern statistical and machine learning techniques in addition to the classical statistics theory and applications. Second, they will learn and polish computational skills for various types of data sets, including large-scale data ubiquitous in business, technology, and science. Third, these two aspects of the program are built on a solid foundation of statistical theory and an understanding of mathematical principles behind techniques and algorithms; this blend is crucial for success in industrial and academic careers in the rapidly changing big data era. Finally, the program has built-in flexibility for different backgrounds and career plans through various electives.
Admission Requirements
Applicants to the program must:
- hold a baccalaureate degree from a regionally accredited institution, or shall have completed equivalent academic preparation as determined by the appropriate campus authority;
- be in good academic standing at the last college or university attended;
- have a 3.0 GPA in their earned undergraduate degree or in the last 60 semester (90 quarter) units completed, or have earned a post-baccalaureate degree;
- have a baccalaureate degree in a quantitative field, including but not limited to statistics, mathematics, computer science, physics, engineering, or relevant fields. Successful applicants are expected to have completed three semesters of calculus, linear algebra, and upper-division undergraduate courses in probability and statistics with a grade of B or better. However, an applicant who is deficient in probability theory and/or statistics may be admitted conditionally on passing MATH 440 Probability and Statistics I and/or MATH 441/741 Probability and Statistics II satisfactorily during the first calendar year of study;
- submit a TOEFL score (minimum 550/80) or IELTS score (minimum 7.0) obtained within the past two years if their undergraduate degree is from a country where the official language is not English.
Program Learning Outcomes
- Apply statistical knowledge and computational skills to formulate problems, plan data collection, and analyze data to provide insight.
- Build and assess statistical and machine learning models, and employ a variety of formal inference procedures.
- Use mathematics to understand the underlying structure of common models used in statistical and machine learning.
- Prepare data for use with a variety of statistical methods and models, and recognize how the quality of data and data collection affect conclusions.
- Communicate effectively to a variety of audiences using oral, written, and visual modes.
Statistical Data Science (M.S.) — 30 Units
Core (15 Units)
Course List | Code | Title | Units |
| MATH 448 | Introduction to Statistical Learning and Data Mining | 3 |
| MATH 742 | Advanced Probability Models | 3 |
| MATH 748 | Theory and Applications of Statistical and Machine Learning | 3 |
| MATH 760 | Multivariate Statistical Methods | 3 |
| MATH 761 | Computational Statistics | 3 |
Upper-Division/Graduate Electives (12 Units)
No more than 9 units should be from undergraduate only courses. Per student’s interested specialization and upon Graduate Advisor’s approval, the student will be recommended to choose a set of electives in the following areas: Probability and Statistics Electives, Mathematics Electives, and Computer Science Electives.
Probability and Statistics Electives
Course List | Code | Title | Units |
| MATH 440 | Probability and Statistics I | 3 |
| MATH 442 | Probability Models | 3 |
| MATH 443 | Introduction to Time Series Analysis | 3 |
| MATH 447 | Design and Analysis of Experiments | 3 |
| MATH 449 | Categorical Data Analysis | 3 |
| MATH 724 | Introduction to Linear Models | 3 |
| MATH 741 | Probability and Statistics II | 3 |
| MATH 899 | Independent Study | 1-3 |
Mathematics Electives
Course List | Code | Title | Units |
| MATH 400 | Numerical Analysis | 3 |
| MATH 425 | Applied and Computational Linear Algebra | 3 |
| MATH 430 | Mathematics of Optimization | 3 |
| MATH 460 | Mathematical Modeling | 3 |
| MATH 495 | Introduction to Wavelets and Frames with Applications | 3 |
| MATH 710 | Measure and Integration | 3 |
| MATH 725 | Advanced Linear Algebra | 3 |
| MATH 771 | Fourier Analysis and Applications | 3 |
| MATH 777 | Partial Differential Equations | 3 |
Computer Science Electives
Course List | Code | Title | Units |
| CSC 821 | Biomedical Imaging and Analysis | 3 |
| CSC 865 | Artificial Intelligence | 3 |
| CSC 869 | Data Mining | 3 |
| CSC 872 | Pattern Analysis and Machine Intelligence | 3 |
| CSC 874 | Topics in Big Data Analysis | 3 |
Culminating Experience (3 Units)
Candidates for the MS in Statistical Data Science must complete a Culminating Experience. Three options are available. Further information for these options can be obtained at the department website http://math.sfsu.edu