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 (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
| Code | Title | Credits |
|---|---|---|
| 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) | ||
| 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 | ||
Degree Requirements
Students are required to have completed their BS/BA degree prior to the first semester as a MS Statistics: Data Science student.
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
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. |
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 | 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
| Code | Title | Credits |
|---|---|---|
| Required Courses: | ||
| STAT 601 | Statistical Methods I | 4 |
| STAT 602 | Statistical Methods II | 4 |
| STAT 610 | Introduction to Statistical Inference | 4 |
| STAT 615 | Statistical Learning | 3 |
| 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 | ||
| 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 Credits | 30 | |
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:
- Bias or Hate Reporting
- Graduate Assistantship Policies and Procedures
- Hostile and Intimidating Behavior Policies and Procedures
- Dean of Students Office (for all students to seek grievance assistance and support)
- Employee Assistance (for personal counseling and workplace consultation around communication and conflict involving graduate assistants and other employees, post-doctoral students, faculty and staff)
- Employee Disability Resource Office (for qualified employees or applicants with disabilities to have equal employment opportunities)
- Graduate School (for informal advice at any level of review and for official appeals of program/departmental or school/college grievance decisions)
- Office of Compliance (for class harassment and discrimination, including sexual harassment and sexual violence)
- Office of Student Conduct and Community Standards (for conflicts involving students)
- Ombuds Office for Faculty and Staff (for employed graduate students and post-docs, as well as faculty and staff)
- Title IX (for concerns about discrimination)
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.
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:
Cecile Ane, Professor
Richard Chappell, Professor
Peter Chien, Professor
Jessi Cisewski-Kehe, Assistant Professor
Nicolas Garcia Trillos, Assistant Professor
Hyunseung Kang, Assistant Professor
Sunduz Keles, Professor
Bret Larget, Professor
Keith Levin, Assistant Professor
Po-Ling Loh, Associate Professor
Wei-Yin Loh, Professor
Michael Newton, Professor
Vivak Patel, Assistant Professor
Sebastian Raschka, Assistant Professor
Garvesh Raskutti, Associate Professor
Karl Rohe, Associate Professor
Kris Sankaran, Assistant Professor
Jun Shao, Professor
Miaoyan Wang, Assistant Professor
Yahzen Wang, Professor
Brian Yandell, Professor
Anru Zhang, Assistant Professor
Chunming Zhang, Professor
Zhengjun Zhang, Professor
Jun Zhu (chair), Professor