This is a named option (formally documented sub-major) professional program in the Statistics M.S. Data science is the study of extracting knowledge from data. Our MS Statistics: Statistics and Data Science option combines a background in statistical theory, methods and practice related to data science with communication skills to train a new generation of leaders who will use data effectively for planning and decision making.

Data science concepts enable students to translate vague questions about complex data into pragmatic analysis steps using statistical thinking. We build from basic methods that compare groups and relate measurements, to more complicated models that depend on the way data are gathered. In practice, planning and decision making involve choices about how to analyze data and communicate findings. These concepts will be grounded at key points with projects that involve real data and/or realistic simulated data.

Please consult the table below for key information about this degree program’s admissions requirements. The program may have more detailed admissions requirements, which can be found below the table or on the program’s website.

Graduate admissions is a two-step process between academic programs and the Graduate School. Applicants must meet the minimum requirements of the Graduate School as well as the program(s). Once you have researched the graduate program(s) you are interested in, apply online.

Fall Deadline March 1
Spring Deadline October 15
Summer Deadline This program does not admit in the summer.
GRE (Graduate Record Examinations) Not 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 (
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: Statistics and Data Science program is intended for three types of students:

  • MS Statistics: Statistics and 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: Statistics and 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: Statistics and 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: Statistics and Data Science students.

Requisites for Admission

Course Requirements - Prerequisite Courses

Students admitted to the MS Statistics: Statistics and 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)
Elementary Matrix and Linear Algebra
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

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 Information

Students enrolled in this program are not eligible to receive tuition remission from graduate assistantship appointments at this institution.

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


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

Mode of Instruction Definitions

Accelerated: Accelerated programs are offered at a fast pace that condenses the time to completion. Students are able to complete a program with minimal disruptions to careers and other commitments.

Evening/Weekend: ​Courses meet on the UW–Madison campus only in evenings and/or on weekends to accommodate typical business schedules.  Students have the advantages of face-to-face courses with the flexibility to keep work and other life commitments.

Face-to-Face: Courses typically meet during weekdays on the UW-Madison Campus.

Hybrid: These programs combine face-to-face and online learning formats.  Contact the program for more specific information.

Online: These programs are offered 100% online.  Some programs may require an on-campus orientation or residency experience, but the courses will be facilitated in an online format.


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 (
Overall Graduate GPA Requirement 3.00 GPA required.
Other Grade Requirements Students may only have one core course (STAT 601, STAT 602, STAT 610, or STAT 615) with a grade below B.
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 substitute a required course (STAT 601, STAT 602, STAT 605, STAT 610, STAT 615, STAT 628) with a Statistics taught 600+ level course 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 500-level or above coursework taught outside of Statistics with advisor approval from the following courses: MATH/​I SY E/​OTM/​STAT  632; COMP SCI 540, 577, 640, 726, 838. Students are not guaranteed a seat in an elective course taught from outside of the Statistics department. They must obtain departmental permission to enroll.
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
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.

Students in this program may not take courses outside the prescribed curriculum without faculty advisor and program director approval. Students in this program cannot enroll concurrently in other undergraduate, graduate or certificate programs.

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.


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.


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


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. They may also contact the L&S Academic Divisional Associate Deans, the L&S Associate Dean for Teaching and Learning Administration, or the L&S Director of Human Resources.



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 Statistics: Statistics and Data Science, M.S. 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.


Cecile Ane, Professor 

Joshua Cape, Assistant Professor

Richard Chappell, Professor 

Peter Chien, Professor

Yinqiu He, Assistant Professor

Jessi Cisewski-Kehe, Assistant Professor

Deshpande, Sameer, Assistant Professor

Nicolas Garcia Trillos, Assistant Professor

Hyunseung Kang, Assistant Professor

Sunduz Keles, Professor 

Bret Larget, Professor

Keith Levin, Assistant Professor

Wei-Yin Loh, Professor 

Michael Newton, Professor 

Vivak Patel, Assistant Professor

Alejandra Quintos, Assistant Professor

Sebastian Raschka, Assistant Professor

Garvesh Raskutti, Associate Professor

Karl Rohe, Professor

Kris Sankaran, Assistant Professor

Jun Shao, Professor 

Miaoyan Wang, Assistant Professor

Yahzen Wang (chair), Professor

Brian Yandell, Professor 

Chunming Zhang, Professor 

Zhengjun Zhang, Professor

Yiqiao Zhong, Assistant Professor

Jun Zhu, Professor