Master of Science

Recommended preparatory courses include advanced undergraduate level courses in linear algebra and probability, and introductory courses in stochastic processes, numerical methods and proficiency in programming (Basic usage of the Python and C/C++ programming languages).

Course fulfillment equivalency

Statistics MS prerequisites

• Multivariable calculus and linear algebra at the level of MATH 51

• Introductory programming at the level of CS 106A

• Intermediate statistics (multiple regression and ANOVA, possibly without linear

  algebra) at the level of STATS 191

• Introductory probability at the level of STATS 117

Data Science subplan prerequisites

• Multivariable calculus and linear algebra at the level of MATH 51

• Introductory programming at the level of CS 106B

• Intermediate statistics (multiple regression and ANOVA, possibly without linear

  algebra) at the level of STATS 191

• Intermediate probability at the level of STATS 118

15 Units

Five (5) Statistics courses must be taken from graduate offerings in the Statistics department at or above the 200-level*.

*Statistics workshop (WKS), seminars (SEM), practicum (PRA), and independent research (INS, RES) credit (1-2 unit) may only count towards breadth/general elective credit. See Breadth section below.

*STATS 200Q is not an allowable course for graduate students.

Breadth courses are meant to complement their statistical training with graduate-level courses from a variety of disciplines. Courses develop relevant skills or emphasize applications of statistics and probability and expand their interests in areas such as optimization, scientific computing, economics, quantitative finance, healthcare, operations research, and environmental science.

Course substitution may require approval from DGS one quarter prior to enrollment.

The following classes are recommended to be taken in sequence:

Year 1, Autumn: STATS 200

Year 1, Winter: STATS 203 and STATS 217

In consultation with the student's program advisor, the student selects 2-3 graduate level courses within the realm of data science to fulfill the remaining coursework required for the degree. Courses are expected to expand and strengthen the student's skill set.

May be taken for letter grade or CR/S.

The capstone project should be computational in nature with real-world project-based research and experiential learning using data science and computation skills learned during the course of their program.

Students should submit a 2-page proposal, supported by the faculty mentor/PI and send to the Data Science program advisor at least one quarter prior to enrolling in any research credit course.

Students are required to take minimum of 3 units of practical component.

Here are some examples of courses that have been used to meet the Capstone requirement:

• DataSci capstone courses: DATASCI 194B, DATASCI 194D, DATASCI 194N

• Analysis and Measurement of Impact (ALP 303)

  • By application; Non-GSB students must apply for and be accepted into the course to enroll.

• Statistical Consulting Workshop (STATS 390) Units: 1. Repeatable up to 3 times.

  • This class requires mastery of Statistics at the (graduate) level necessary to provide consultation to fellow members of the university.

  • Students attend weekly lectures on Friday to discuss consulting cases and various statistical techniques that arise frequently in consulting.

• Consulting Workshop on Biomedical Data Science (BMDS 291A) Units: 1. Repeatable up to 3 times.

  • The Data Studio is a collaboration between Spectrum (The Stanford Center for Clinical and Translational research and Education) and the Department of Biomedical Data Science (DBDS). The educational goal of this workshop is to provide data science consultation training for students.

• Workshop in Biomedical Data Science (STATS 260ABC/BMDS 280ABC opens in a new tab) Repeatable up to 3 times.

  • Applications of data science techniques to current problems in biology, medicine and healthcare. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write a two page critical summary of one of the workshops, with the choice made by the student.

• Xplore (CME 291) Project Based Research Course opens in a new tab Units: 1 - 6.

  • Enrollment by application only - limited enrollment; priority is given to ICME students.

Research for Credit

• Independent Study (STATS 299) Units: 1-3; Repeatable up to 6 units.

  • In consultation with your program advisor, and permission of statistics faculty/advisor students may formulate independent study or directed reading in statistics, data science or related field. Students should submit a one-page proposal, supported by the faculty member and sent to the student's program advisor for approval (one quarter prior to start of the project).

  • or equivalent course in statistics adjacent department with program advisor approval.

• Research in The Computational Neuroscience Laboratory opens in a new tab (PSYC 399) Units: 3. Repeatable up to 6 units.

  • Enrollment by application only.

  • Stanford students interested in research at the intersection of artificial intelligence, medical image analysis, and computational neuroscience to collaborate with our group in multiple exciting projects.

  • The research can be performed as an independent study at the CNSLab (part of the School of Medicine).

• Industrial Research for Statisticians (STATS 298) Units: 1. Repeatable up to 3 units.

  • International students (F-1) MUST enroll when employed by a U.S. company (CPT opens in a new tab).

  • Students MUST submit one-page summary of the job offer to their advisor prior to enrolling in STATS 298 and applying for CPT.

  • Students MUST submit one-page report of their internship to advisor by the end of the quarter.