STATS-MS - Statistics (MS)
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Program Overview
The MS in Statistics and the MS in Statistics, Data Science track are intended as terminal degree programs and do not lead to the PhD program in Statistics. Students interested in pursuing doctoral study in Statistics should apply directly to the PhD program.
Admissions Information
Prospective applicants should consult the Graduate Admissions and the Statistics Department admissions webpages for complete information on admission prerequisites, application requirements and deadlines.
Applicants apply to the Master of Science degree program in Statistics. Applicants applying to the Statistics Data Science subplan will select Data Science under the Specialization field. Leave this field blank if you do not intend to pursue the Data Science subplan.
Current Stanford students interested in applying to the Data Science subplan in Statistics must apply as external candidates.
Visit Graduate Admissions to start an application.
Coterminal Master’s Program
Stanford undergraduates who want to apply for the coterminal master’s degree in Statistics must submit a complete application to the department by the deadline published on the department’s coterminal admissions webpage.
Applications are accepted three times each year, summer, autumn, and winter quarters for autumn, winter, and spring quarter start, respectively. The general GRE is not required of coterminal applicants.
Students pursuing the Statistics coterminal master’s degree must follow the same curriculum requirements stated in the Requirements for the Master of Science in Statistics section.
University Coterminal Requirements
Coterminal master’s degree candidates are expected to complete all master’s degree requirements as described in this Bulletin. Coterminal Master’s Program discusses university requirements for the coterminal master’s degree. Graduate Degrees discusses university requirements for the MS degree.
After accepting admission to this coterminal master’s degree program, students may request a transfer of eligible courses from the undergraduate to the graduate career to satisfy the requirements for the master’s degree. Transferring courses to the graduate career requires review and approval of both the undergraduate and graduate programs on a case-by-case basis.
Minimum Units in the Program
Minimum University Units
Master's Degree Program Proposal
The Statistics Master's Degree Program Proposal form must be signed and approved by the department's student services administrator before submission to the student's program advisor. This form is due no later than the end of the first quarter of enrollment in the program. A revised program proposal must be submitted if degree plans change. There is no thesis requirement.
Minimum Progress
As defined in the general graduate student requirements, students must maintain a 3.0 ('B') grade point average overall in courses applicable to the degree.
With the exception of STATS 118; Math 104, Math 113; CS 106B, CS 107, CME 108, classes must be taken at the 200 level or higher.
Graduate Degrees discusses the university’s basic requirements for the MS degree. The following are specific departmental requirements.
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 course
Introductory programming at the level of course
Intermediate statistics (multiple regression and ANOVA, possibly without linear
algebra) at the level of course
Introductory probability at the level of course
Data Science subplan prerequisites
Prerequisite courses may not be counted towards the 45 units required of the MS.
Courses below 200 level are not acceptable with the following exceptions: STATS 118; Math 104, Math 113; CS 106B, CS 107, CS140-182, CME 108.
Students must take two advanced courses in probability or stochastic processes when replacing both STATS 118 and STATS 217.
Students may NOT enroll in STATS 118 AFTER completion of any of the following: STATS 200, 218, 219, 300A, 310A.
Must be taken for a letter grade.
Must be taken for a letter grade.
Students may NOT enroll in STATS 200 AFTER completion of any course in the STATS 300 series.
Must be taken for a letter grade.
Must be taken for a letter grade.
Substitution of other courses in Linear Algebra may be made with consent of the advisor.
Must be taken for a letter grade.
Students who have these skills may elect a more advanced CS course and may require consent of the program advisor.
Must be taken for a letter grade.
15 Units
Five (5) additional Statistics courses must be taken from graduate offerings in the Statistics department at or above the 200-level*.
Must be taken for a letter grade, except those offered CR/S.
*Statistics workshop (WKS), training seminars (SEM), 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 that provide the application of or a range of other disciplines to the degree may be chosen as elective units to complete the degree requirements. The advisor may authorize other graduate courses (200 or above) if they provide skills relevant to degree requirements or deal primarily with an application of statistics or probability and do not significantly overlap (repeat) courses in the student’s program.
There is sufficient flexibility to accommodate students with interests in applications to business, computing, economics, engineering, health, operations research, and biological and social sciences.
Courses below 200 level are not acceptable, with the following exceptions below.
Courses below 200 level are not acceptable, with the following exceptions below.
Any of the courses from the following course set may be counted toward the total units for the degree.
Except the following Workshop, Training Seminars, and Independent Research courses.
For these courses, students may enroll in up to 6-units (combined) of the following workshop, training seminar, independent research credit to fulfill breadth/elective coursework (1-unit).
Select three courses (3 units each).
If necessary, remainder of units may be fulfilled by workshops, training, independent study, seminars, etc.
The following classes are recommended to be taken in sequence:
Year 1, Autumn: STATS 200
Year 1, Winter: STATS 203 and STATS 217
Must be taken for a letter grade.
Must be taken for a letter grade.
Must be taken for a letter grade.
Must be taken for a letter grade.
Must be taken for a letter grade.
Students may also petition to use other classes that are focused on optimization, scientific computing and/or large-scale data analyses for this requirement.
Must be taken for a letter grade.
Students may also petition to use other classes that fulfill ML or AI learning objectives.
Must be taken for a letter grade.
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.
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:
Analysis and Measurement of Impact (course)
By application; Non-GSB students must apply for and be accepted into the course to enroll.
Statistical Consulting Workshop (course) 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 (course) 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.
Xplore (course) Project Based Research Course Units: 1 - 6.
Enrollment by application only - limited enrollment; priority is given to ICME students.
Industrial Research for Statisticians (course) Units: 1. Repeatable up to 3 units.
International students (F-1) MUST enroll when employed by a U.S. company (CPT).
Students MUST submit one-page summary of the job offer to their advisor prior to enrolling in STATS 298 and applying for CPT.
Research for Credit
Independent Study (course) Units: 1-3 - Repeatable up to 6 units.
In consultation with your M.S, advisor, independent study/directed reading with permission of statistics faculty/advisor. Students should submit a one-page proposal, supported by the faculty member and sent to the student's Data Science advisor for approval (one quarter prior to start of the project).
Research in The Computational Neuroscience Laboratory (course) 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).
Courses for the Capstone requirement may be taken for letter grade or CR/S.