This is a named option (formally documented sub-major) professional program in the Statistics MS. 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.
Students may also be interested in the MS Data Science professional program, offered by the Department of Statistics in cooperation with Department of Computer Sciences. The MS Data Science program is designed for students who are primarily interested in entering the data science profession, and teaches key computational and statistical skills that may be applied to a variety of industries.
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 | February 15 |
| Spring Deadline | October 1 |
| 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 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 |
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 applicants:
- MS Statistics: Statistics and Data Science for Visiting International Student Program (VISP) students
- Students from the Visiting International Student Program (Stat VISP or Math VISP) who have completed some degree requirements at UW-Madison as Visiting International Student Program undergraduates. They may request transfer of up to 15 credits from their Visiting International Student Program 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
| Code | Title | Credits |
|---|---|---|
| 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) | ||
| Linear algebra: | ||
| 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 | ||
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 Information
Students enrolled in this program are not eligible to receive tuition remission from graduate assistantship appointments at this institution.
Additional information about funding and scholarships for MS Statistics: Statistics and Data Science is available on the program website.
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 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 | Students may only have one core course (STAT 601, STAT 610, or STAT 615) with a grade below B. |
| Assessments and Examinations | None. |
| Language Requirements | No language requirements. |
Required Courses
| Code | Title | Credits |
|---|---|---|
| Core | 11 | |
| Statistical Methods I | ||
| Introduction to Statistical Inference | ||
| Statistical Learning | ||
| Professional Skills Courses | 6 | |
| Data Science Computing Project | ||
| Data Science Practicum | ||
or STAT 678 | Introduction to Statistical Consulting | |
| Students who are able to demonstrate equivalent prior coursework and/or experience may request to substitute a required course (STAT 601, STAT 605, STAT 610, STAT 615, STAT 628) with a Statistics-taught course numbered 600 or above with advisor approval. Substitutions are not guaranteed and will be reviewed on a case-by-case basis. | ||
| Elective Courses | 13 | |
| Students may count up to 3 credits of STAT electives numbered 300-599 including: | ||
| R for Statistics I | ||
| R for Statistics II | ||
| R for Statistics III | ||
| Introduction to Time Series | ||
| Introductory Nonparametric Statistics | ||
| Data Science Computing Project | ||
| An Introduction to Sample Survey Theory and Methods | ||
| Applied Categorical Data Analysis | ||
| Data Science with R | ||
| Statistical Data Visualization | ||
| 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 1 elective course (up to 4 credits) of coursework numbered 500 or above taught outside of STAT 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 STAT coursework numbered 600 or above including the following: | ||
| Computing in Data Science and Statistics | ||
| 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 | ||
| Linear Randomized Algorithms for Data Science | ||
| Bayesian Statistics | ||
| 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 | ||
| Theory and Application of Regression and Analysis of Variance I | ||
| Theory and Application of Regression and Analysis of Variance II | ||
| Estimation of Functions from Data | ||
| Statistical Methods for Molecular Biology | ||
| Seminar | ||
| To reach the minimum of 13 credits for electives, students may also count the following courses: | ||
| R for Statistics I | ||
| R for Statistics II | ||
| R for Statistics III | ||
| Internship Course in Comp Sci and Data Science (1 credit maximum allowed) | ||
| Professional Skills in Data Science | ||
| Directed Study (2 credits maximum allowed) | ||
| 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 to fulfill the MS in Statistics and Data Science requirements, but if both courses are to be used, the undergraduate level course must be completed first. 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 and STAT 701, STAT 351 and STAT 809, STAT 405 and STAT 605, STAT 411 and STAT 732, STAT 456 and STAT 760, STAT 443 and STAT 761, STAT 451 and STAT 615, and STAT/COMP SCI 471 and STAT 771. 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 or graduate degree 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 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 five 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 7 credits from a UW–Madison undergraduate degree are allowed to transfer toward the minimum graduate degree credit requirement. Coursework earned five 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 numbered 300 or above are allowed to transfer toward 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 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 Limits
Students are expected to complete the program in 2 semesters (if coming from the Statistics Visiting International Student Program 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
- 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
Not applicable.
Professional Development
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, MS 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.