This is a named option in the Statistics MS.
Admissions
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 | January 2 |
| 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 exclusively in English, must provide an English proficiency test score earned within two years of the anticipated term of enrollment. Refer to the Graduate School: Minimum Requirements for Admission policy: https://policy.wisc.edu/library/UW-1241. |
| Other Test(s) (e.g., GMAT, MCAT) | n/a |
| Letters of Recommendation Required | 3 |
Applicants holding a bachelor's degree with a natural science, social science, or engineering major and strong mathematical background are encouraged to apply for admission to the graduate program in statistics. Applicants are advised to undertake graduate work in statistics only if their undergraduate grades in mathematics were uniformly high.
Funding
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
Each option within Statistics has different funding policies and opportunities for students. Please see each option for 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 | No |
Mode of Instruction Definitions
Accelerated: Accelerated programs are offered at a fast pace that condenses the time to completion. Students typically take enough credits aimed at completing the program in a year or two.
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 | 15 credits must be graduate-level coursework. Refer to the Graduate School: Minimum Graduate Coursework (50%) Requirement policy: https://policy.wisc.edu/library/UW-1244. |
| Overall Graduate GPA Requirement | 3.00 GPA required. Refer to the Graduate School: Grade Point Average (GPA) Requirement policy: https://policy.wisc.edu/library/UW-1203. |
| 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 | Students must pass a competency test containing both a written and an oral component, demonstrating that they have the potential to be a practicing statistician. |
| Language Requirements | No language requirements. |
Required Courses
| Code | Title | Credits |
|---|---|---|
| Core | ||
| STAT 609 | Mathematical Statistics I | 3 |
| or STAT/MATH 709 | Mathematical Statistics | |
| STAT 610 | Introduction to Statistical Inference | 4 |
| or STAT/MATH 710 | Mathematical Statistics | |
| STAT 849 | Theory and Application of Regression and Analysis of Variance I | 3 |
| STAT 850 | Theory and Application of Regression and Analysis of Variance II | 3 |
| STAT 998 | Statistical Consulting | 3 |
| Select 6 or more credits of STAT courses 600 or higher 1 | ||
| Must include 6 elective credits in: | ||
| STAT/B M I 641 | Statistical Methods for Clinical Trials | 3 |
And | ||
| STAT/B M I 642 | Statistical Methods for Epidemiology | 3 |
| or STAT/B M I 741 | Survival Analysis Theory and Methods | |
| or STAT/B M I 877 | Statistical Methods for Molecular Biology | |
| The following will also be allowed to count toward the 30-credit minimum for the master's degree (with permission of the Director of Graduate Studies) | ||
| Up to 6 credits from STAT Courses Numbered: | 6 | |
| 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 | ||
| Data Science with R | ||
| 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 | ||
| Introduction to Combinatorics | ||
| Special Topics in Statistics | ||
| Linear Optimization | ||
| Statistical Methods for Spatial Data | ||
| Courses that cover the same or similar topic at the undergraduate- and graduate-level may both be used towards the MS requirements. If both courses are to be used, the undergraduate level course must be completed first for both courses to be counted. Otherwise, only the graduate level course will be counted. 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 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. | ||
| Up to 6 credits of graduate courses outside of STAT in consultation with advisor. | 0-6 | |
| Up to 6 credits of STAT 699 in consultation with advisor. | 0-6 | |
| Total Credits | 30 | |
- 1
Courses that do not satisfy this requirement are: STAT 601 Statistical Methods I, STAT 602 Statistical Methods II, STAT 609 Mathematical Statistics I, STAT 610 Introduction to Statistical Inference, STAT 628 Data Science Practicum, STAT 678 Introduction to Statistical Consulting, STAT 699 Directed Study, STAT/MATH 709 Mathematical Statistics, STAT/MATH 710 Mathematical Statistics, STAT 849 Theory and Application of Regression and Analysis of Variance I, STAT 850 Theory and Application of Regression and Analysis of Variance II, or STAT 998 Statistical Consulting
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 Credits Earned at Other Institutions
With program approval, students are allowed to transfer no more than 9 credits of graduate coursework from other institutions toward the graduate degree credit and graduate coursework (50%) requirements. Coursework earned ten or more years prior to admission to a master’s degree is not allowed to satisfy requirements.
Undergraduate Credits Earned at Other Institutions or UW-Madison
With program approval, up to 6 Statistics credits from a UW–Madison undergraduate degree in coursework numbered 600 or above are allowed to transfer for minimum graduate degree credits. Coursework earned ten or more years prior to admission to a master’s degree is not allowed to satisfy requirements.
Credits Earned as a Professional Student at UW-Madison (Law, Medicine, Pharmacy, and Veterinary careers)
Refer to the Graduate School: Transfer Credits for Prior Coursework policy.
Credits Earned as a University Special Student at UW–Madison
With program approval, up to 14 Statistics credits completed at UW–Madison while a University Special student in coursework numbered 300 or above are allowed to transfer for the minimum graduate degree credit requirement. Of these credits, those numbered 700 or above or are taken to meet the requirements of a capstone certificate and has the "Grad 50%" attribute may also transfer for the minimum graduate coursework (50%) requirement. Coursework earned ten or more years prior to admission to a master’s degree is not allowed to satisfy requirements.
Probation
Three consecutive reviews in which a student fails to meet the minimum criteria for satisfactory progress will result in the student being dropped from the program. Contact the program for more information.
Advisor / Committee
Students are required to meet with their advisor near the beginning of each semester to discuss course selection and progress.
Credits Per Term Allowed
15 credits
Time Limits
The competency test must be passed within six semesters after entering the department.
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
- 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 Student Assistance and Support (OSAS) (for all students to seek grievance assistance and support)
- 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
Students pursuing the general statistics and biostatistics options are considered for department financial support and may seek a dual degree if desired.
Professional Development
Graduate School Resources
Take advantage of the Graduate School's professional development resources to build skills, thrive academically, and launch your career.
People
Faculty
Cecile Ane, Professor
Joshua Cape, Assistant Professor
Richard Chappell, Professor
Peter Chien, Professor
Jessi Cisewski-Kehe, Assistant Professor
Sameer Deshapande, Assistant Professor
Rishabh Dudeja, Assistant Professor
Nicolas Garcia Trillos, Assistant Professor
Chris Geoga, Assistant Professor
Yongyi Guo, Assistant Professor
Yinqiu He, Assistant Professor
Hyunseung Kang, Associate Professor
Matthias Katzfuss, Professor
Sunduz Keles, Professor
Bret Larget (chair), Professor
Ben Lengerich, Assistant Professor
Keith Levin, Assistant Professor
Wei-Yin Loh, Professor
Michael Newton, Professor
Vivak Patel, Assistant Professor
Debdeep Pati, Professor
Alejandra Quintos, Assistant Professor
Garvesh Raskutti, Associate Professor
Karl Rohe, Professor
Kris Sankaran, Assistant Professor
Jun Shao, Professor
Miaoyan Wang, Assistant Professor
Yahzen Wang, Professor
Yuling Yan, Assistant Professor
Chunming Zhang, Professor
Yiqiao Zhong, Assistant Professor
Jun Zhu, Professor