Department of Mathematics

some math pictures

The M.S. Program in Statistical Data Science

The Master of Science in Statistical Data Science at San Francisco State offers a comprehensive curriculum in the fields of statistics and data science under the guidance of a faculty encompassing many different areas of research to prepare students with diverse backgrounds (including statistics, mathematics, computer science, engineering, and other quantitative fields) for the data science workforce. The program curriculum emphasizes the following four aspects: First, students will train in depth in modern statistical and machine learning techniques in addition to 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 statistics theory and understanding of mathematical principles behind techniques and algorithms. Finally, the program has built-in flexibility for different backgrounds and career plans through various electives. Classes are small, usually fewer than 20.

The expected completion time for the program is two years. After graduation, students are expected to seek employment as data scientists/analysts in the San Francisco Bay Area's technology, pharmaceutical, and financial industries.

San Francisco State University is a large comprehensive urban university enrolling about 7000 graduate students in 124 different Master's degree and credential programs. The campus is located in an unusually vibrant and beautiful city with a rich intellectual and cultural life. Graduates find opportunities in nearby Silicon Valley, home to many of the world's leading technology companies, in downtown San Francisco's large financial and insurance industry, and in a thriving biotechnology sector.

San Francisco State University is strongly committed to achieving excellence through cultural diversity. Applications are encouraged from underrepresented groups.

For more general information, contact the Statistical Data Science Graduate Coordinators Dr. Mohammad Kafai or Dr. Alexandra Piryatinksa. You are encouraged to contact individual faculty members if you have specific questions about their research activities.

Admission Requirements & Application Procedures

Admission to the program requires an undergraduate degree but not necessarily in statistics. 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.

Information on how to apply along with on-line application forms are available from the Graduate School. Applicants must contact all colleges and universities where they have been enrolled and order official transcripts for SFSU. Applicants must submit at least two letters of recommendation. These letters may be from academic supervisors, past employers or anyone else who can address the applicant's qualifications for graduate study. Applicants may submit these with their applications.

Application Deadlines

  • Priority Deadline for the Fall semester is March 1. The Mathematics Department will continue to review applications on a space available basis.
  • Priority Deadline for the Spring semester is October 1. The Mathematics Department will continue to review applications on a space available basis.

General information about graduate studies at San Francisco State University can be obtained from the university web page or by calling 415.338.2234. International students should contact the Office of International Programs, San Francisco State University, San Francisco, CA, 94132, USA. Their phone number is 415.338.1293, and they have a very informative web page.

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