Master of Science in Data Science
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Download the degree guide for details on admission requirements, tuition, and courses.
Advance Your Career with an Online Master’s in Data Science from the University of Wisconsin
Data is shaping the future—and skilled data science professionals are in demand across every industry. Whether you’re a tech-savvy professional looking to level up or a career-changer ready for something new, the University of Wisconsin’s 100% online Master’s in Data Science gives you the flexibility, skills, and credibility to move forward—on your terms.
Ready to take the next step? Request information and discover how UW can help you reach your goals.
Built for Working Adults, Backed by a Respected University
The University of Wisconsin Master’s in Data Science is designed for professionals ready to turn curiosity into expertise and ambition into action. Learn when and where it works for you—while gaining real-world skills that today’s employers are actively seeking.
As one of the first online data science programs in the nation, the University of Wisconsin has set the standard for quality and innovation in online education. Built by expert faculty and industry leaders, our rigorous curriculum reflects years of refinement and real-world relevance—delivering a respected credential backed by the trusted UW name.
Why Choose UW’s MS in Data Science?
- 100% Online and Asynchronous
Learn on your schedule, from anywhere—no required log-in times.
- Hands-on, Career-Ready Skills
Build expertise in Python, machine learning, predictive modeling, data visualization, cloud computing, and more.
- Strong Industry Connections
Learn from experience faculty and connect with a network of data professionals.
- A Degree Employers Recognize
Earn your degree from one of the nation’s most trusted and respected public universities.
During the interview process, my MSDS degree provided me with a wealth of knowledge and insights to draw upon, enabling me to engage deeply with the topics discussed. The skills I acquired through this program, particularly in Python, R, and data visualization tools, are proving to be the cornerstone of my role here. I am thrilled that my education equipped me with the most advanced and industry-relevant technologies, setting me up for success in this dynamic field.” –Pranali Shendekar, Database Analyst, Medical College of Wisconsin, Data Science Program Graduate
Earn More, Do More, Be More
A master’s dgree in data science can unlock high-paying roles in fast-growing fields like healthcare, finance, tech, and beyond. Our graduates go on to lead data teams, guide strategy, and build solutions that shape the future. Titles include:
- Data Scientist
- Business Intelligence Analyst/Architect
- Data Engineer
- Data Analyst
- Programmer Analyst
- Database Developer/Engineer
- Healthcare/Research Analyst
- Machine Learning Engineer
- Financial Analyst
- Data Warehouse Architect
- Marketing Analyst
- Software Engineers
- Solutions/Systems Architect
Not Ready to do the Master’s? Start with the Graduate Certificate
Our Data Science Graduate Certificate is a 5-course, 15-credit program built for working professionals. You’ll gain in-demand skills with the freedom to choose electives that align with your goals. Plus, if you decide to continue, your certificate credits can apply directly toward a master’s degree.
Your Future Starts Here
There’s never been a better time to invest in yourself. The demand for data science talent is growing, and UW’s flexible, affordable, and fully online program is here to help you make your move.
Request information or connect with an enrollment adviser today.
Universities of Wisconsin Collaboration
The Master of Science in Data Science is a partnership of UW-Eau Claire, UW-Green Bay, UW-La Crosse, UW-Oshkosh, UW-Stevens Point, and UW-Superior. Learn more about our campus partners and choosing a home campus.
Accreditation
Whether online or on campus, University of Wisconsin programs have a reputation for delivering world-class education and student support. Accreditation is your assurance that you will graduate with skills that are relevant to your field and valued by employers. The Master of Science in Data Science is approved by the University of Wisconsin Board of Regents and is fully accredited by the Higher Learning Commission.
To be eligible for admission to the UW Data Science Master’s program, students must meet the following requirements:
- A bachelor’s degree from a regionally accredited university (or its equivalent) with a cumulative GPA of 3.0 or higher. Students with a GPA of less than 3.0 may be considered for provisional admission based on a review of all application materials.
- Completed coursework in elementary statistics and introductory computer programming. Relevant work experience in these areas may be considered in lieu of prerequisite coursework. Please contact an enrollment adviser for details.
You will also need:
- Your resume.
- A personal statement of up to 1,000 words describing the reasons behind your decision to pursue this degree and what you believe you will bring to the data science field. Space for the personal statement is included in the online application.
Aptitude tests, such as the GMAT or GRE, are not required for admission.
If you are not sure whether you meet these requirements, or which courses you need to take to satisfy prerequisites, contact an enrollment adviser by phone, 608-800-6762, or email learn@uwex.wisconsin.edu.
Application Deadline
Your online application and all required materials must be submitted to your preferred home campus generally 2-4 weeks prior to the date classes start (this varies by campus) to be considered for admission.
Starting your application early will ensure you have plenty of time to gather required materials (such as transcripts) and complete the University of Wisconsin System Online Admission Application.
International Guidelines
This program welcomes online students from around the world. Online students do not qualify for an F-1 Student Visa to travel to the U.S. but instead can participate in our online courses remotely. If your native language is not English and/or you attended school outside of the U.S., you will likely need to provide proof of English language proficiency and an official translation or evaluation of academic transcripts. Requirements will vary based on a student’s academic history and home campus policies. For guidance about these requirements and how they apply to your specific situation, contact your preferred home campus admissions office.
If you would like to apply as an International Student for an on-campus program in the UW System, please refer to these resources through Go Wisconsin.
How to Apply
While you are free to apply on your own, many prospective students find it helpful to speak with an enrollment adviser first.
Step 1: Decide which home campus you’d like to apply to. Campus partners for the Data Science master’s program are UW-Eau Claire, UW-Green Bay, UW-La Crosse, UW-Oshkosh, UW-Stevens Point, and UW-Superior.
Step 2: Visit the University of Wisconsin System Online Admission Application. Login or create an account, apply to the home campus of your choice, and choose the “Data Science -Collaborative” program.
- If you are applying to UW-Eau Claire for Spring 2025 or later, you will use the UW-Eau Claire Graduate Admissions Application.
A nonrefundable $56 application fee is required for most graduate degree-seeking students applying to a UW System institution.*
*For a limited time, UW is offering an application fee waiver to those who haven’t yet applied to the Spring, Summer or Fall 2026 semesters. To redeem, use coupon code APPLY26 on the UW Online Application payment page.
Step 3: Send your resume, personal statement, and arrange to have your official college transcripts (from each institution you attended) sent to the graduate student admissions office of the home campus to which you applied.
*Please request electronic transcripts if this service is offered by your previous school(s). Have the e-transcript sent from your previous school directly to the admissions e-mail address of your chosen home campus. E-transcripts are usually delivered more quickly than physical copies sent by mail.
UW Data Science Courses Feature Innovative Interdisciplinary Curriculum
The UW Master of Data Science program offers a well-rounded curriculum grounded in computer science, math and statistics, management, and communication. All course content, from multimedia lectures and e-learning tools to homework assignments, are delivered through the program’s online learning management system. You can study and do homework whenever and wherever it’s convenient for you. Learn more about online learning with UW.
Students in the master’s program are required to take 10 courses, including a capstone project course typically taken during the final semester. In the capstone course, students gain valuable, real-world experience through a fieldwork project. Projects may be at their current place of employment or with an external organization. Program faculty, academic advisers, and advisory board members are a rich source of industry connections for projects. View examples of past capstone projects.
Interested in the 5-course UW Graduate Certificate in Data Science? Take a look at the certificate courses here.
Preview lectures, assignments, and discussions in this Course Inside Look: Foundations of Data Science.
| Course | Credits |
|---|---|
This course introduces data science and highlights its importance in decision making. Students will learn how to analyze data using the R programming language. During the course, students will learn how to import data into R, tidy it, conduct exploratory data analysis, develop visualizations, and draw statistical inferences. The course aims to teach data wrangling, visualization and exploration with R. | 3 Credits |
This course will present statistical methods and inference procedures with an emphasis on applications, computer implementation, and interpretation of results. Familiarity with the R programming language is highly recommended. Topics include simple and multiple regression, model selection, correlation, moderation/interaction analysis, logistic regression, the chi-square test, the Kruskal-Wallis test, analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), factor analysis, and canonical correlation analysis. Prerequisite: DS 700 or 701. | 3 Credits |
Introduction to programming languages and packages used in data science. | 3 Credits |
This course explores the various approaches for data management used in data science. We present how data is collected, transformed, stored, and delivered for use in data science projects. | 3 Credits |
This course prepares you to process large data sets efficiently. You will be introduced to nonrelational databases and algorithms that allow for the distributed processing of large data sets across clusters. Prerequisite: DS 710 | 3 Credits |
Explore data mining methods and procedures for diagnostic and predictive analytics. Topics include association rules, clustering algorithms, tools for classification, and ensemble methods. Computer implementation and applications will be emphasized. Prerequisites: DS 705 or DS 710. (Starting in Fall 2026, DS 705 will be the required prerequisite). | 3 Credits |
Data storytelling involves using data to tell a compelling narrative that helps audiences understand, engage with, and act on the information. This course combines data analysis with communication techniques to present data in an informative and engaging way. This course is specifically designed as a graduate-level requirement for the MSDS degree, focusing on teaching students how to effectively communicate insights through data storytelling techniques. Participants will learn to craft engaging stories that resonate with various audiences and drive decision-making. Prerequisites: DS 700 or 701. DS 705 OR DS 740 suggested but not required. | 3 Credits |
This course examines how data science relates to developing strategies for organizations. The emphasis is on using an organization’s data assets to inform better decisions. The course investigates the use of data science findings to develop solutions to competitive organizational challenges. Special attention is given to critically examining decisions to ensure that they are ethical and avoid unfair bias. Professional codes of conduct as well as local and international regulations are also considered. Prerequisites: DS 740 suggested but not required. | 3 Credits |
Introduction to the theory and applications of deep learning. The course begins with the study of neural networks and how to train them. Various deep learning architectures are introduced including convolutional neural networks, recurrent neural networks, and transformers. Applications may include image classification, object detection, and natural language processing. Algorithms will be implemented in Python using a high-level framework such as Pytorch or TensorFlow. Prerequisites: DS 740; DS 710 preferred. (Starting in Fall 2026, DS 710 will be required in addition to DS 740). | 3 Credits |
Students will develop and execute a data science project using real-world data and communicate results to non-technical audiences. Prerequisites: DS715 or DS716, DS730, DS740, DS750 or completion of 27 credits. | 3 Credits |
Course availability for the UW Data Science program varies each fall, spring, and summer. Course offerings are subject to change due to fluctuating enrollments. If you are a current student, please consult with your campus adviser prior to registration.
Finish your degree at your pace. Take as many courses per semester as your schedule allows.
Interested in the 5-course UW Graduate Certificate in Data Science? Take a look at the certificate course schedule here.
Spring 2026
Course Preview Week: January 20 - January 26, 2026
Semester Dates: January 27 - May 08, 2026
| Course | Credits |
|---|---|
This course introduces data science and highlights its importance in decision making. Students will learn how to analyze data using the R programming language. During the course, students will learn how to import data into R, tidy it, conduct exploratory data analysis, develop visualizations, and draw statistical inferences. The course aims to teach data wrangling, visualization and exploration with R. | 3 Credits |
This course will present statistical methods and inference procedures with an emphasis on applications, computer implementation, and interpretation of results. Familiarity with the R programming language is highly recommended. Topics include simple and multiple regression, model selection, correlation, moderation/interaction analysis, logistic regression, the chi-square test, the Kruskal-Wallis test, analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), factor analysis, and canonical correlation analysis. Prerequisite: DS 700 or 701. | 3 Credits |
Introduction to programming languages and packages used in data science. | 3 Credits |
This course explores the various approaches for data management used in data science. We present how data is collected, transformed, stored, and delivered for use in data science projects. | 3 Credits |
This course prepares you to process large data sets efficiently. You will be introduced to nonrelational databases and algorithms that allow for the distributed processing of large data sets across clusters. Prerequisite: DS 710 | 3 Credits |
Explore data mining methods and procedures for diagnostic and predictive analytics. Topics include association rules, clustering algorithms, tools for classification, and ensemble methods. Computer implementation and applications will be emphasized. Prerequisites: DS 705 or DS 710. (Starting in Fall 2026, DS 705 will be the required prerequisite). | 3 Credits |
Data storytelling involves using data to tell a compelling narrative that helps audiences understand, engage with, and act on the information. This course combines data analysis with communication techniques to present data in an informative and engaging way. This course is specifically designed as a graduate-level requirement for the MSDS degree, focusing on teaching students how to effectively communicate insights through data storytelling techniques. Participants will learn to craft engaging stories that resonate with various audiences and drive decision-making. Prerequisites: DS 700 or 701. DS 705 OR DS 740 suggested but not required. | 3 Credits |
This course examines how data science relates to developing strategies for organizations. The emphasis is on using an organization’s data assets to inform better decisions. The course investigates the use of data science findings to develop solutions to competitive organizational challenges. Special attention is given to critically examining decisions to ensure that they are ethical and avoid unfair bias. Professional codes of conduct as well as local and international regulations are also considered. Prerequisites: DS 740 suggested but not required. | 3 Credits |
Introduction to the theory and applications of deep learning. The course begins with the study of neural networks and how to train them. Various deep learning architectures are introduced including convolutional neural networks, recurrent neural networks, and transformers. Applications may include image classification, object detection, and natural language processing. Algorithms will be implemented in Python using a high-level framework such as Pytorch or TensorFlow. Prerequisites: DS 740; DS 710 preferred. (Starting in Fall 2026, DS 710 will be required in addition to DS 740). | 3 Credits |
Students will develop and execute a data science project using real-world data and communicate results to non-technical audiences. Prerequisites: DS715 or DS716, DS730, DS740, DS750 or completion of 27 credits. | 3 Credits |
Summer 2026
Registration Opens: March 09, 2026
Course Preview Week: May 19 - May 25, 2026
Semester Dates: May 26 - August 07, 2026
| Course | Credits |
|---|---|
This course introduces data science and highlights its importance in decision making. Students will learn how to analyze data using the R programming language. During the course, students will learn how to import data into R, tidy it, conduct exploratory data analysis, develop visualizations, and draw statistical inferences. The course aims to teach data wrangling, visualization and exploration with R. | 3 Credits |
Introduction to programming languages and packages used in data science. | 3 Credits |
Explore data mining methods and procedures for diagnostic and predictive analytics. Topics include association rules, clustering algorithms, tools for classification, and ensemble methods. Computer implementation and applications will be emphasized. Prerequisites: DS 705 or DS 710. (Starting in Fall 2026, DS 705 will be the required prerequisite). | 3 Credits |
Data storytelling involves using data to tell a compelling narrative that helps audiences understand, engage with, and act on the information. This course combines data analysis with communication techniques to present data in an informative and engaging way. This course is specifically designed as a graduate-level requirement for the MSDS degree, focusing on teaching students how to effectively communicate insights through data storytelling techniques. Participants will learn to craft engaging stories that resonate with various audiences and drive decision-making. Prerequisites: DS 700 or 701. DS 705 OR DS 740 suggested but not required. | 3 Credits |
Students will develop and execute a data science project using real-world data and communicate results to non-technical audiences. Prerequisites: DS715 or DS716, DS730, DS740, DS750 or completion of 27 credits. | 3 Credits |
Fall 2026
Registration Opens: April 13, 2026
Course Preview Week: September 01 - September 07, 2026
Semester Dates: September 08 - December 18, 2026
| Course | Credits |
|---|---|
This course introduces data science and highlights its importance in decision making. Students will learn how to analyze data using the R programming language. During the course, students will learn how to import data into R, tidy it, conduct exploratory data analysis, develop visualizations, and draw statistical inferences. The course aims to teach data wrangling, visualization and exploration with R. | 3 Credits |
This course will present statistical methods and inference procedures with an emphasis on applications, computer implementation, and interpretation of results. Familiarity with the R programming language is highly recommended. Topics include simple and multiple regression, model selection, correlation, moderation/interaction analysis, logistic regression, the chi-square test, the Kruskal-Wallis test, analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), factor analysis, and canonical correlation analysis. Prerequisite: DS 700 or 701. | 3 Credits |
Introduction to programming languages and packages used in data science. | 3 Credits |
This course explores the various approaches for data management used in data science. We present how data is collected, transformed, stored, and delivered for use in data science projects. | 3 Credits |
This course prepares you to process large data sets efficiently. You will be introduced to nonrelational databases and algorithms that allow for the distributed processing of large data sets across clusters. Prerequisite: DS 710 | 3 Credits |
Explore data mining methods and procedures for diagnostic and predictive analytics. Topics include association rules, clustering algorithms, tools for classification, and ensemble methods. Computer implementation and applications will be emphasized. Prerequisites: DS 705 or DS 710. (Starting in Fall 2026, DS 705 will be the required prerequisite). | 3 Credits |
This course examines how data science relates to developing strategies for organizations. The emphasis is on using an organization’s data assets to inform better decisions. The course investigates the use of data science findings to develop solutions to competitive organizational challenges. Special attention is given to critically examining decisions to ensure that they are ethical and avoid unfair bias. Professional codes of conduct as well as local and international regulations are also considered. Prerequisites: DS 740 suggested but not required. | 3 Credits |
Students will develop and execute a data science project using real-world data and communicate results to non-technical audiences. Prerequisites: DS715 or DS716, DS730, DS740, DS750 or completion of 27 credits. | 3 Credits |
Spring 2027
Registration Opens: November 09, 2026
Course Preview Week: January 19 - January 25, 2027
Semester Dates: January 26 - May 07, 2027
| Course | Credits |
|---|---|
This course introduces data science and highlights its importance in decision making. Students will learn how to analyze data using the R programming language. During the course, students will learn how to import data into R, tidy it, conduct exploratory data analysis, develop visualizations, and draw statistical inferences. The course aims to teach data wrangling, visualization and exploration with R. | 3 Credits |
This course will present statistical methods and inference procedures with an emphasis on applications, computer implementation, and interpretation of results. Familiarity with the R programming language is highly recommended. Topics include simple and multiple regression, model selection, correlation, moderation/interaction analysis, logistic regression, the chi-square test, the Kruskal-Wallis test, analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), factor analysis, and canonical correlation analysis. Prerequisite: DS 700 or 701. | 3 Credits |
Introduction to programming languages and packages used in data science. | 3 Credits |
This course explores the various approaches for data management used in data science. We present how data is collected, transformed, stored, and delivered for use in data science projects. | 3 Credits |
Data storytelling involves using data to tell a compelling narrative that helps audiences understand, engage with, and act on the information. This course combines data analysis with communication techniques to present data in an informative and engaging way. This course is specifically designed as a graduate-level requirement for the MSDS degree, focusing on teaching students how to effectively communicate insights through data storytelling techniques. Participants will learn to craft engaging stories that resonate with various audiences and drive decision-making. Prerequisites: DS 700 or 701. DS 705 OR DS 740 suggested but not required. | 3 Credits |
This course examines how data science relates to developing strategies for organizations. The emphasis is on using an organization’s data assets to inform better decisions. The course investigates the use of data science findings to develop solutions to competitive organizational challenges. Special attention is given to critically examining decisions to ensure that they are ethical and avoid unfair bias. Professional codes of conduct as well as local and international regulations are also considered. Prerequisites: DS 740 suggested but not required. | 3 Credits |
Introduction to the theory and applications of deep learning. The course begins with the study of neural networks and how to train them. Various deep learning architectures are introduced including convolutional neural networks, recurrent neural networks, and transformers. Applications may include image classification, object detection, and natural language processing. Algorithms will be implemented in Python using a high-level framework such as Pytorch or TensorFlow. Prerequisites: DS 740; DS 710 preferred. (Starting in Fall 2026, DS 710 will be required in addition to DS 740). | 3 Credits |
Students will develop and execute a data science project using real-world data and communicate results to non-technical audiences. Prerequisites: DS715 or DS716, DS730, DS740, DS750 or completion of 27 credits. | 3 Credits |
Students completing the Masters of Science in Data Science will be able to:
Manage and prepare data.
- Collect, prepare, store and manage data to devise solutions to data science tasks.
- Manage and use data in various forms, from traditional databases to big data.
Transform data into insights.
- Determine the conditions for when a predictive and prescriptive model is applicable.
- Design and implement algorithms to translate data into actionable insights.
Communicate solutions.
- Create, write, and orally communicate technical materials for diverse audiences
- Help technical and non-technical professionals visualize, explore, interpret, and act on data science findings.
Ethics and decision making.
- Identify and utilize data assets to enhance organizational effectiveness.
- Identify and analyze ethical issues in data science and apply a professional code of conduct.
Graduate Tuition
Tuition is a flat fee of $875 per credit whether you live in Wisconsin or out of state, and financial aid is available for students who qualify.
There are no additional course or program fees, however, textbooks are purchased separately and are not included in tuition. You will not pay segregated fees (fees in addition to tuition that cover the cost of student-organized activities, facility maintenance, and operations) and you will not be charged a technology fee. If software or special technology is required in one of your courses, it will be provided to you and is included in your tuition.
Financial Aid
Financial aid may be available to you and is awarded by your home campus. Contact your home campus financial aid office to see if you qualify for aid as a full or part-time student.
Visit our financial aid page to learn more about FAFSA and other sources of financial aid.
Veteran Benefits
Benefits are available to qualifying veterans and those currently serving. Contact your home campus veteran services office for details.
UW Grants and Scholarships
You may be eligible for a grant or scholarship as a student in a semester-based collaborative program. More information can be found here.
Most Affordable Online Master’s in Data Science Programs, TechGuide, ranked in 2026.
The University of Wisconsin’s online Graduate Certificate in Data Science offers a flexible, career-focused option designed for working professionals. The curriculum includes three foundational courses that cover essential data science concepts, plus two electives you choose based on your goals—allowing you to tailor the certificate to your interests or industry needs. In just five courses, you’ll gain practical, in-demand skills to analyze data, uncover insights, and make data-driven decisions. Plus, all 15 credits can apply toward the online Master of Science in Data Science if you decide to pursue the full degree later. Take a look at the certificate courses here.
Experience UW Data Science
Learn about data science, meet the faculty, read student stories, and more. Read the blog.