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The M.S. Data Science is a joint professional program between the Statistics and Computer Sciences Departments and is administered by the Statistics Department.  The program provides students with abilities in computational and statistical thinking and skills, which may be combined with domain knowledge to address data-rich problems from diverse fields and various industries. Graduates will acquire data science competencies to think critically about data, and to manage, process, model and analyze data to obtain meaning and knowledge, and further to use data in responsible, ethical ways. The curriculum addresses emerging, and rapidly growing areas of applied statistical and computing research and practice. Graduates seek employment as data analysts and data scientists or pursue further education in data science, statistics, computer science, or related quantitative and computational fields.

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 15*
Spring Deadline The program does not admit in the spring.
Summer Deadline The 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 2 required, 3 recommended

For the Fall 2022 term, applications will be accepted until July 15, 2022.


Applicants to the MS Data Science program should have completed the following courses equivalent to the UW-Madison courses listed below:

Calculus and Mathematical Foundation, complete all below
MATH 221 Calculus and Analytic Geometry 15
MATH 222 Calculus and Analytic Geometry 24
MATH 340 Elementary Matrix and Linear Algebra3
Programming Foundation, select one from the list below
COMP SCI 220 Data Science Programming I4
COMP SCI 300 Programming II3
COMP SCI 320 Data Science Programming II4
Recommended previous coursework of significant experience in R
STAT 303
STAT 304
STAT 305
R for Statistics I
and R for Statistics II
and R for Statistics III
STAT 433 Data Science with R3

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.

Major 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 15 credits must be graduate-level coursework. Details can be found in the Graduate School’s Minimum Graduate Coursework (50%) policy (
Overall Graduate GPA Requirement 3.00 GPA required.
This program follows the Graduate School's policy:
Other Grade Requirements None.
Assessments and Examinations None.
Language Requirements No language requirements.


Statistics Core, complete all 3 courses below9
Statistical Models for Data Science
Statistical Inference for Data Science
Statistical Methods for Data Science
Computer Sciences Core, select 1 course from each category9
Introduction to Optimization
Introduction to Algorithms
Nonlinear Optimization I
Introduction to Operating Systems
Database Management Systems: Design and Implementation
Introduction to Computer Networks
Introduction to Information Security
Distributed Systems
Big Data Systems
Topics in Database Management Systems
Humans and Data
Data Visualization
Human-Computer Interaction
Machine Learning Core, select 2 courses from the list below6
Introduction to Artificial Intelligence
Machine Learning
Mathematical Foundations of Machine Learning
Advanced Deep Learning
Introduction to Machine Learning and Statistical Pattern Classification
Introduction to Deep Learning and Generative Models
Statistical Learning
Data Science Electives, select 6 credits from the courses below 16
Introduction to Optimization
Introduction to Operating Systems
Database Management Systems: Design and Implementation
Introduction to Bioinformatics
Introduction to Algorithms
Introduction to Computer Networks
Introduction to Information Security
Graduate Cooperative Education
Nonlinear Optimization I
Advanced Operating Systems
Distributed Systems
Big Data Systems
Security and Privacy for Data Science
Topics in Database Management Systems
Data Visualization
Computer Vision
Advanced Natural Language Processing
Human-Computer Interaction
Foundations of Data Management
Master's Research
Theoretical Foundations of Machine Learning
Data and Algorithms: Ethics and Policy
R for Statistics I
and R for Statistics II
and R for Statistics III
Introduction to Time Series
Introductory Nonparametric Statistics
An Introduction to Sample Survey Theory and Methods
Applied Categorical Data Analysis
Data Science with R
Classification and Regression Trees
Applied Multivariate Analysis
Financial Statistics
Introduction to Computational Statistics
Statistical Methods for Spatial Data
Applied Time Series Analysis, Forecasting and Control I
Multivariate Analysis I
Decision Trees for Multivariate Analysis
Statistical Computing
Simulation Modeling and Analysis
Stochastic Modeling Techniques
Stochastic Programming
Dynamic Programming and Associated Topics
Integer Optimization
Total Credits30

Courses listed both as core course and as an elective may count toward either the requirement, but not both.

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.

Major-Specific Policies


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 STAT 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 STAT 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.


This program follows the Graduate School's Probation policy.


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


15 credits

TIME Limits

Students are expected to complete the program in 3-4 semesters.  Students who wish to pursue the program part time must receive permission from the program chair.


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.


Not applicable.

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 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.

  1. Demonstrates understanding of theories, methodologies, and computation as tools to solve complex problems in data science.
  2. Selects or adapts appropriate data science approaches and uses or develops best practices in data-driven applications.
  3. Synthesizes information, organizes insights, and evaluates impact pertaining to questions for studies involving empirical data.
  4. Communicates data science concepts and results clearly.
  5. Adheres to principles of ethical and professional conduct in data science.

MDS Program Committee

Remzi Arpaci-Dusseau, Professor and CS Department Chair

Bret Larget, Professor

Yong Jae Lee, Associate Professor

Yazhen Wang, Professor and Stat Department Chair

Sara Rodock, Academic Advising Assistant Director

Jinda Moore, Professional Programs Specialist