This is a named option (formally documented sub-major) professional program in the Statistics M.S.

Fall Deadline March 1
Spring Deadline October 15
Summer Deadline This program does not admit in the summer.
GRE (Graduate Record Examinations) Required.
English Proficiency Test Every applicant whose native language is not English or whose undergraduate instruction was not in English must provide an English proficiency test score and meet the Graduate School minimum requirements (https://grad.wisc.edu/apply/requirements/#english-proficiency).
Other Test(s) (e.g., GMAT, MCAT) n/a
Letters of Recommendation Required 3

Students with questions regarding the programs admission rules and standards should visit our application website.

The MS Statistics: Data Science program is intended for three types of students:

  • MS Statistics: Data Science for VISP students: Students from the Visiting International Student Program (Stat VISP or Math VISP) who have completed some degree requirements at UW-Madison as VISP undergraduates. They may request transfer of up to 15 credits from their VISP coursework.
  • MS Statistics: Data Science for workforce students: Students coming with 5 or more years in the workforce who have worked extensively with data and are seeking a well-rounded training. Some students may be part-time students (6-8 credits per semester) if they remain in the workforce.
  • MS Statistics: Data Science for other general students:  Students who have BS degrees or expected to obtain BS degrees prior to the first semester as MS Statistics: Data Science students.

Requisites for Admission

Course Requirements - Prerequisite Courses

Students admitted to the MS Statistics: Data Science program are expected to have courses equivalent to the UW-Madison courses listed below.
4 semesters of calculus:
Calculus and Analytic Geometry 1
Calculus and Analytic Geometry 2
Calculus--Functions of Several Variables
The Theory of Single Variable Calculus (or another advanced analysis course)
MATH 340 Elementary Matrix and Linear Algebra3
It is highly recommended that students also have:
R for Statistics I
R for Statistics II
Introduction to Probability and Mathematical Statistics I
Introduction to Probability and Mathematical Statistics II

Degree Requirements

Students are required to have completed their BS/BA degree prior to the first semester as a MS Statistics: Data Science students.

Graduate School Resources

Resources to help you afford graduate study might include assistantships, fellowships, traineeships, and financial aid. Further funding information is available from the Graduate School. Be sure to check with your program for individual policies and restrictions related to funding.

Program Resources

The M.S. Statistics: Data Science option is unique and does not allow students to accept a tuition remitting assistantship, hold multiple positions that would result in tuition remission, or to be concurrently enrolled in another university program or enrolled in courses outside of the M.S. Statistics: Data Science curriculum—see here for more details.

Minimum Graduate School Requirements

Review the Graduate School minimum academic progress and degree requirements, in addition to the program requirements listed below.

Named Option Requirements

MODE OF INSTRUCTION

Face to Face Evening/Weekend Online Hybrid Accelerated
Yes No No No Yes

Mode of Instruction Definitions

CURRICULAR REQUIREMENTS

Minimum Credit Requirement 30 credits
Minimum Residence Credit Requirement 16 credits
Minimum Graduate Coursework Requirement Half of degree coursework (15 credits out of 30 total credits) must be completed graduate-level coursework; courses with the Graduate Level Coursework attribute are identified and searchable in the university's Course Guide (https://registrar.wisc.edu/course-guide/).
Overall Graduate GPA Requirement 3.00 GPA required.
Other Grade Requirements A grade of B or better must be received in any course used to fulfill the required and elective course requirements.
Assessments and Examinations None.
Language Requirements No language requirements.

Required COURSES

Required Courses:
STAT 601 Statistical Methods I4
STAT 602 Statistical Methods II4
STAT 610 Introduction to Statistical Inference4
STAT 615 Statistical Learning3
Professional Skills Courses (6 credits minimum from the following courses):6
Data Science Computing Project
Professional Skills in Data Science
Data Science Practicum
Students may substite STAT 605 or STAT 615 with STAT 609 with advisor approval
9 elective credits:9
Students may count up to 3 credits of Statistics undergraduate electives including:
R for Statistics I
R for Statistics II
R for Statistics III
Introduction to Time Series
Introductory Nonparametric Statistics
An Introduction to Sample Survey Theory and Methods
Applied Categorical Data Analysis
Classification and Regression Trees
Introduction to Machine Learning and Statistical Pattern Classification
Introduction to Deep Learning and Generative Models
Applied Multivariate Analysis
Financial Statistics
Introduction to Computational Statistics
Special Topics in Statistics
Statistical Methods for Spatial Data
Students may count up to 3 credits of 600-level or above coursework taught outside of Statistics with advisor approval, including courses cross listed with Statistics but taught by other departments
Student must have at least 3 credits of coursework at the 600-level or above taught within Statistics including the following:
Mathematical Statistics I
Statistical Methods for Clinical Trials
Statistical Methods for Epidemiology
Special Topics in Statistics (may be repeated with different topic titles)
Applied Time Series Analysis, Forecasting and Control I
Mathematical Statistics
Mathematical Statistics
Large Sample Theory of Statistical Inference
Survival Analysis Theory and Methods
Multivariate Analysis I
Decision Trees for Multivariate Analysis
Statistical Methods for Medical Image Analysis
Statistical Computing
Introduction to Bayesian Decision and Control I
Experimental Design I
Non Parametric Statistics
Sample Survey Theory and Method
Empirical Processes and Semiparametric Inference
Statistical Model Building and Learning
Nonparametric Statistics and Machine Learning Methods
Estimation of Functions from Data
Statistical Methods for Molecular Biology
Seminar
Total Credits30

Graduate and Undergraduate Courses with Similar Topics

Courses that cover the same or similar topic at the undergraduate- and graduate-level may both be used towards the MSDS requirements, but if both courses are to be used, the undergraduate level course must be completed first. Please note that this policy does not preclude students from taking just the undergraduate or just the graduate version of a topic.  These combinations would include STAT 349 Introduction to Time Series and STAT 701 Applied Time Series Analysis, Forecasting and Control I, STAT 351 Introductory Nonparametric Statistics and STAT 809 Non Parametric Statistics, STAT 411 An Introduction to Sample Survey Theory and Methods and STAT 732 Large Sample Theory of Statistical Inference, STAT 456 Applied Multivariate Analysis and STAT 760 Multivariate Analysis I, STAT 443 Classification and Regression Trees and STAT 761 Decision Trees for Multivariate Analysis, STAT 451 Introduction to Machine Learning and Statistical Pattern Classification and STAT 615 Statistical Learning, and STAT/​COMP SCI  471 Introduction to Computational Statistics and STAT 771 Statistical Computing. This will also apply to special topics courses that have similar topics between the undergraduate and graduate level.

Graduate School Policies

The Graduate School’s Academic Policies and Procedures provide essential information regarding general university policies. Program authority to set degree policies beyond the minimum required by the Graduate School lies with the degree program faculty. Policies set by the academic degree program can be found below.

Named Option-Specific Policies

Prior Coursework

Graduate Work from Other Institutions

With program approval, students are allowed to count no more than 9 credits of graduate coursework from other institutions toward the graduate degree credit and graduate coursework (50%) requirements. Coursework earned five or more years prior to admission to a master’s degree is not allowed to satisfy requirements.

UW–Madison Undergraduate

With program approval, up to 7 statistics credits from a UW–Madison undergraduate degree are allowed to count toward minimum graduate degree credits. Coursework earned five or more years prior to admission to a master’s degree is not allowed to satisfy requirements.

UW–Madison University Special

With program approval, up to 15 statistics credits completed at UW–Madison while a University Special student at the 300 level or above are allowed to count toward minimum graduate degree and graduate residence credit requirements. Of these credits, those at the 700 level or above may also count toward the minimum graduate coursework (50%) requirement. Coursework earned five or more years prior to admission to a master’s degree is not allowed to satisfy requirements.

Probation

Students are required to follow all of the requirements listed in the program handbook for maintaining satisfactory academic program.  In particular, students must maintain a 3.0 GPA and have a minimum grade of B for any course used to satisfy program requirements.  Students who do not make satisfactory academic progress for multiple semesters may be dismissed from the program.

ADVISOR / COMMITTEE

Students are required to communicate with their advisor near the beginning of each semester to discuss course selection and progress.

CREDITS PER TERM ALLOWED

15 credits

Time Constraints

Students are expected to complete the program in 2 semesters (if coming from the Statistics VISP program) or 3-4 semesters.  Students who wish to pursue the program part time must receive permission from the program chair.

Grievances and Appeals

These resources may be helpful in addressing your concerns:

Students should contact the department chair or program director with questions about grievances.

Other

The MS Statistics: Data Science option is unique and does not allow students to accept a tuition remitting assistantship, hold multiple positions that would result in tuition remission, or to be concurrently enrolled in another university program or enrolled in courses outside of the MS Statistics: Data Science curriculum—see here for more details.

Graduate School Resources

Take advantage of the Graduate School's professional development resources to build skills, thrive academically, and launch your career. 

Program Resources

Students in the M.S. Statistics: Data Science program are encouraged to participate in program specific professional development events and work directly, one-on-one, with advisors as well.   Information about events and resources will be made available to currently enrolled students via email.

Faculty: Professors J. Zhu (chair), Ane, Chappell, Chien, Keles, Larget, W-Y Loh, Newton, Shao, Y. Wang, Yandell, C. Zhang, Z. Zhang; Associate Professors P-L Loh, Raskutti, Rohe; Assistant Professors Garcia Trillos, Kang, Patel, Raschka, M. Wang, A. Zhang