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 (https://grad.wisc.edu/apply/requirements/#english-proficiency). |
| Other Test(s) (e.g., GMAT, MCAT) | n/a |
| Letters of Recommendation Required | 2 required, 3 recommended |
REQUISITES FOR ADMISSION
Applicants to the MS Data Science program should have completed the following courses equivalent to the UW-Madison courses listed below:
| Code | Title | Credits |
|---|---|---|
| Calculus and Mathematical Foundation, complete all below | ||
| MATH 221 | Calculus and Analytic Geometry 1 | 5 |
| MATH 222 | Calculus and Analytic Geometry 2 | 4 |
| MATH 340 | Elementary Matrix and Linear Algebra | 3 |
| Programming Foundation, select one from the list below | ||
| COMP SCI 220 | Data Science Programming I | 4 |
| COMP SCI 300 | Programming II | 3 |
| COMP SCI 320 | Data Science Programming II | 4 |
| 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 | 3 |
| STAT 433 | Data Science with R | 3 |
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
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. Details can be found in the Graduate School’s Minimum Graduate Coursework (50%) policy (https://policy.wisc.edu/library/UW-1244). |
| Overall Graduate GPA Requirement | 3.00 GPA required. This program follows the Graduate School's policy: https://policy.wisc.edu/library/UW-1203. |
| Other Grade Requirements | None. |
| Assessments and Examinations | None. |
| Language Requirements | No language requirements. |
REQUIRED COURSES
| Code | Title | Credits |
|---|---|---|
| Statistics Core, complete all 3 courses below | 9 | |
| Statistical Models for Data Science | ||
| Statistical Inference for Data Science | ||
| Statistical Methods for Data Science | ||
| Computer Sciences Core, select 1 course from each category | 9 | |
| Algorithms | ||
| Introduction to Optimization | ||
| Introduction to Algorithms | ||
| Nonlinear Optimization I | ||
| Systems | ||
| 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 below | 6 | |
| 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 1 | 6 | |
| 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 Credits | 30 | |
- 1
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 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.
Major-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 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 numbered 300 or above are allowed to count toward minimum graduate degree and graduate residence credit requirements. Of these credits, those numbered 700 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
This program follows the Graduate School's Probation policy.
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 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
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.
- Demonstrates understanding of theories, methodologies, and computation as tools to solve complex problems in data science.
- Selects or adapts appropriate data science approaches and uses or develops best practices in data-driven applications.
- Synthesizes information, organizes insights, and evaluates impact pertaining to questions for studies involving empirical data.
- Communicates data science concepts and results clearly.
- Adheres to principles of ethical and professional conduct in data science.