The MS 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.
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 | 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 | 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 | 2 |
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 |
| or MATH 345 | Linear Algebra and Optimization | |
| 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 |
Funding
Graduate School Resources
The Bursar’s Office provides information about tuition and fees associated with being a graduate student. 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 for MS Data Science is available on the program website.
Minimum Graduate School Requirements
Review the Graduate School minimum degree requirements and policies, 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. 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 | None. |
| Assessments and Examinations | None. |
| Language Requirements | No language requirements. |
Required Courses
| Code | Title | Credits |
|---|---|---|
| Statistics Core | ||
| STAT 611 | Statistical Models for Data Science | 3 |
| STAT 612 | Statistical Inference for Data Science | 3 |
| STAT 613 | Statistical Methods for Data Science | 3 |
| Computer Sciences Core | ||
| Complete 1 course from each category for a total of 9 credits | 9 | |
| Algorithms | ||
| Introduction to Optimization | ||
| Introduction to Algorithms | ||
| Nonlinear Optimization I | ||
| Systems | ||
| Introduction to Operating Systems | ||
| Introduction to Big Data 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 | ||
| Complete 2 courses from the list below for a total of 6 credits | 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 | ||
| Complete 6 credits from the courses below 1 | 6 | |
| Introduction to Optimization | ||
| Introduction to Operating Systems | ||
| Introduction to Big Data 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 | ||
| Trustworthy Artificial Intelligence | ||
| Topics in Database Management Systems | ||
| Data Visualization | ||
| Computer Vision | ||
| Advanced Natural Language Processing | ||
| Human-Computer Interaction | ||
| Data Exploration, Cleaning, and Integration for Data Science | ||
| Foundations of Data Management | ||
| Master's Research (3 credits maximum of COMP SCI 799 and/or STAT 699 allowed) | ||
| 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 | ||
| Internship Course in Comp Sci and Data Science | ||
| 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 | ||
| Statistics in Human Genetics | ||
| Directed Study (3 credits maximum of STAT 699 and/or COMP SCI 799 allowed) | ||
| Applied Time Series Analysis, Forecasting and Control I | ||
| Multivariate Analysis I | ||
| Decision Trees for Multivariate Analysis | ||
| Computational Statistics | ||
| Bayesian Statistics | ||
| Simulation Modeling and Analysis | ||
| Stochastic Modeling Techniques | ||
| Stochastic Programming | ||
| Dynamic Programming and Associated Topics | ||
| Integer Optimization | ||
| Data-Driven Dynamical Systems, Stochastic Modeling and Prediction | ||
| Total Credits | 30 | |
- 1
Courses listed both as core course and as an elective may satisfy either 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 serve as the official document of record for Graduate School academic and administrative policies and procedures and are updated continuously. Note some policies redirect to entries in the official UW-Madison Policy Library. Programs may set more stringent policies than the Graduate School. Policies set by the academic degree program can be found below.
Major-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 minimum graduate degree credits. Coursework earned five or more years prior to admission to a master’s degree is not allowed to satisfy requirements. This program does not accept undergraduate credits from other institutions.
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 credits completed at UW–Madison while a University Special student numbered 300 or above are allowed to transfer toward minimum graduate degree requirements. Of these credits, those numbered 700 or above may also transfer to fulfill 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
Refer to the Graduate School: 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 credit maximum. Refer to the Graduate School: Maximum Credit Loads and Overload Requests policy.
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
- 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)
L&S Policy for Graduate Student Academic Appeals
Graduate students have the right to appeal an academic decision related to an L&S graduate program if the student believes that the decision is inconsistent with published policy.
Academic decisions that may be appealed include:
- Dismissal from the graduate program
- Failure to pass a qualifying or preliminary examination
- Failure to achieve satisfactory academic progress
- Academic disciplinary action related to failure to meet professional conduct standards
Issues such as the following cannot be appealed using this process:
- A faculty member declining to serve as a graduate student’s advisor.
- Decisions regarding the student’s disciplinary knowledge, evaluation of the quality of work, or similar judgements. These are the domain of the department faculty.
- Course grades. These can be appealed instead using the L&S Policy for Grade Appeal.
- Incidents of bias or hate, hostile and intimidating behavior, or discrimination (Title IX, Office of Compliance). Direct these to the linked campus offices appropriate for the incident(s).
Appeal Process for Graduate Students
A graduate student wishing to appeal an academic decision must follow the process in the order listed below. Note time limits within each step.
- The student should first seek informal resolution, if possible, by discussing the concern with their academic advisor, the department’s Director of Graduate Studies, and/or the department chair.
- If the program has an appeal policy listed in their graduate program handbook, the student should follow the policy as written, including adhering to any indicated deadlines. In the absence of a specific departmental process, the chair or designee will be the reviewer and decision maker, and the student should submit a written appeal to the chair within 15 business days of the academic decision. The chair or designee will notify the student in writing of their decision.
- If the departmental process upholds the original decision, the graduate student may next initiate an appeal to L&S. To do so, the student must submit a written appeal to the L&S Assistant Dean for Graduate Student Academic Affairs within 15 business days of notification of the department’s decision.
- To the fullest extent possible, the written appeal should include, in a single document: a clear and concise statement of the academic decision being appealed, any relevant background on what led to the decision, the specific policies involved, the relief sought, any relevant documentation related to the departmental appeal, and the names and titles of any individuals contributing to or involved in the decision.
- The Assistant Dean will work with the Academic Associate Dean of the appropriate division to consider the appeal. They may seek additional information and/or meetings related to the case.
- The Assistant Dean and Academic Associate Dean will provide a written decision within 20 business days.
- If L&S upholds the original decision, the graduate student may appeal to the Graduate School. More information can be found on their website: Grievances and Appeals (see: Graduate School Appeal Process).
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 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.
Learning Outcomes
- 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.