Master of Science in Data Science
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Download the degree guide for details on admission requirements, tuition, and courses.
Big Data, Big Opportunities for You and Your Career
The world is generating data at an astonishing rate—about 2.5 quintillion bytes each day. Employers are racing to hire professionals who know how to interpret and extract meaning from data. The University of Wisconsin Master of Science in Data Science program is 100 percent online, designed for busy adults eager to learn how to clean, organize, analyze, and interpret data to drive business insights. Discover the latest tools and analytical methods to effectively work with and communicate about data.
Discover Exciting, High-Paying Career Opportunities
Data science is one of the fastest-growing professions of the 21st century, with the potential to impact nearly every sector of the global economy. A UW Master of Science in Data Science can be the foundation for a variety of lucrative occupations. Many of our graduates achieve director, manager and senior level positions in an array of data fields, including:
- 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
RELATED: Data Science Careers
Who Should Apply?
The Master of Science in Data Science program is designed for anyone interested in working with data. You do not need prior data experience to be admitted. Our students come from a wide variety of backgrounds, including computer science, business, mathematics, engineering, statistics, and marketing.
Busy adults will find the flexibility of the online format especially convenient. Learn more about online learning with UW.
UW Data Science offers an online Data Science Graduate Certificate. The certificate is an ideal way to build your knowledge and abilities with the relevant data science skills to thrive in today’s data-driven world. You have the option of applying the graduate certificate credits to the master’s degree if you choose to further your studies.
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 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, introductory computer programming, and introduction to databases. Relevant work experience in these areas may be considered in lieu of prerequisite coursework. If you are in need of prerequisite coursework, this pre-approved list of options may assist you. Please contact an enrollment adviser for details.
You will also need:
- Your resume.
- Two letters of recommendation (can be professional or academic).
- 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 UW-HELP.
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 2025 semesters. To redeem, use coupon code APPLY25 on the UW Online Application payment page.
Step 3: Send your resume, personal statement, and letters of recommendation; 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.
Students in the master’s program are required to take 12 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 provides an introduction to data science and highlights its importance in business decision making. It provides an overview of commonly used data science tools along with spreadsheets, relational databases, statistics, and programming assignments to lay the foundation for data science applications. | 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 | 3 Credits |
Computer programming is an essential part of data science. When working with large data sets, it’s especially important to be able to write effective, efficient code to help you organize and understand the data. In this course, we’ll introduce you to one of the most widely-used programming languages for data science: Python. You’ll gain experience working with real-world data, and leave the course with skills you can apply in other courses in the MS Data Science Program as well as on the job! | 3 Credits |
This course will introduce you to the concepts and techniques to work with and reason about subject-oriented, integrated, time-variant, and nonvolatile collections of data in support of management’s decision-making process. | 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 |
This course will prepare you to master technical, informational, and persuasive communication to meet organizational goals. Technical communication topics include a study of the nature, structure, and interpretation of data. Informational communication topics include data visualization and design of data for understanding and action. Persuasive communication topics include the study of written, verbal, and nonverbal approaches to influencing decision makers. | 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 700, DS 710 | 3 Credits |
This course covers two aspects of data analytics. First, it teaches techniques to generate visualizations appropriate to the audience type, task, and data. Second, it teaches methods and techniques for analyzing unstructured data – including text mining, web text mining and social network analysis. Prerequisite: DS 740 | 3 Credits |
Investigate the ethical issues in computer science that ultimately also pertain to data science, including privacy, plagiarism, intellectual property rights, piracy, security, confidentiality, and many other issues. Your study of these issues will begin broadly, with a look at ethical issues in computer science at large. We will then make inferences to the narrower field of data science. We will consider ethical arguments and positions, the quality and integrity of decisions and inferences based on data, and how important cases and laws have shaped the legality, if not the morality, of data science-related computing. We will use case studies to investigate these issues. Prerequisite: DS 740 | 3 Credits |
Note: Fall 2024 is the last semester DS 775 will be offered. Explore procedures and techniques for using data to inform the decision-making process. Topics include optimization, decision analysis, game theory, and simulation. Prerequisites: DS 705, DS 710 | 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 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. Prerequisite: DS 740 | 3 Credits |
Explore the current and future applications of data science as a strategic decision-making tool to achieve a competitive advantage in business. With an emphasis on obtaining decision-making value from an organization’s data assets, this course will investigate the use of data science findings to develop solutions to competitive business challenges. Through case studies, you will examine how data science methods can support business decision making, and discover a range of methods the data scientist can use to get people within the organization on board with data science projects. | 3 Credits |
Students will develop and execute a data science project using real-world data and communicate results to non-technical audiences. Prerequisites: DS 715, DS 730, DS 735, DS 745, DS 775 or DS 776 | 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.
Interested in the 5-course UW Graduate Certificate in Data Science? Take a look at the certificate course schedule here.
Fall 2024
Course Preview Week: August 27 - September 02, 2024
Semester Dates: September 03 - December 13, 2024
| Course | Credits |
|---|---|
This course provides an introduction to data science and highlights its importance in business decision making. It provides an overview of commonly used data science tools along with spreadsheets, relational databases, statistics, and programming assignments to lay the foundation for data science applications. | 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 | 3 Credits |
Computer programming is an essential part of data science. When working with large data sets, it’s especially important to be able to write effective, efficient code to help you organize and understand the data. In this course, we’ll introduce you to one of the most widely-used programming languages for data science: Python. You’ll gain experience working with real-world data, and leave the course with skills you can apply in other courses in the MS Data Science Program as well as on the job! | 3 Credits |
This course will introduce you to the concepts and techniques to work with and reason about subject-oriented, integrated, time-variant, and nonvolatile collections of data in support of management’s decision-making process. | 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 700, DS 710 | 3 Credits |
This course covers two aspects of data analytics. First, it teaches techniques to generate visualizations appropriate to the audience type, task, and data. Second, it teaches methods and techniques for analyzing unstructured data – including text mining, web text mining and social network analysis. Prerequisite: DS 740 | 3 Credits |
Investigate the ethical issues in computer science that ultimately also pertain to data science, including privacy, plagiarism, intellectual property rights, piracy, security, confidentiality, and many other issues. Your study of these issues will begin broadly, with a look at ethical issues in computer science at large. We will then make inferences to the narrower field of data science. We will consider ethical arguments and positions, the quality and integrity of decisions and inferences based on data, and how important cases and laws have shaped the legality, if not the morality, of data science-related computing. We will use case studies to investigate these issues. Prerequisite: DS 740 | 3 Credits |
Note: Fall 2024 is the last semester DS 775 will be offered. Explore procedures and techniques for using data to inform the decision-making process. Topics include optimization, decision analysis, game theory, and simulation. Prerequisites: DS 705, DS 710 | 3 Credits |
Students will develop and execute a data science project using real-world data and communicate results to non-technical audiences. Prerequisites: DS 715, DS 730, DS 735, DS 745, DS 775 or DS 776 | 3 Credits |
Spring 2025
Request Permission NumberCourse Preview Week: January 21 - January 27, 2025
Semester Dates: January 28 - May 09, 2025
| Course | Credits |
|---|---|
This course provides an introduction to data science and highlights its importance in business decision making. It provides an overview of commonly used data science tools along with spreadsheets, relational databases, statistics, and programming assignments to lay the foundation for data science applications. | 3 Credits |
Computer programming is an essential part of data science. When working with large data sets, it’s especially important to be able to write effective, efficient code to help you organize and understand the data. In this course, we’ll introduce you to one of the most widely-used programming languages for data science: Python. You’ll gain experience working with real-world data, and leave the course with skills you can apply in other courses in the MS Data Science Program as well as on the job! | 3 Credits |
This course will introduce you to the concepts and techniques to work with and reason about subject-oriented, integrated, time-variant, and nonvolatile collections of data in support of management’s decision-making process. | 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 |
This course will prepare you to master technical, informational, and persuasive communication to meet organizational goals. Technical communication topics include a study of the nature, structure, and interpretation of data. Informational communication topics include data visualization and design of data for understanding and action. Persuasive communication topics include the study of written, verbal, and nonverbal approaches to influencing decision makers. | 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 700, DS 710 | 3 Credits |
This course covers two aspects of data analytics. First, it teaches techniques to generate visualizations appropriate to the audience type, task, and data. Second, it teaches methods and techniques for analyzing unstructured data – including text mining, web text mining and social network analysis. Prerequisite: DS 740 | 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 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. Prerequisite: DS 740 | 3 Credits |
Explore the current and future applications of data science as a strategic decision-making tool to achieve a competitive advantage in business. With an emphasis on obtaining decision-making value from an organization’s data assets, this course will investigate the use of data science findings to develop solutions to competitive business challenges. Through case studies, you will examine how data science methods can support business decision making, and discover a range of methods the data scientist can use to get people within the organization on board with data science projects. | 3 Credits |
Students will develop and execute a data science project using real-world data and communicate results to non-technical audiences. Prerequisites: DS 715, DS 730, DS 735, DS 745, DS 775 or DS 776 | 3 Credits |
Summer 2025
Registration Opens: March 10, 2025
Course Preview Week: May 20 - May 26, 2025
Semester Dates: May 27 - August 08, 2025
| Course | 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 | 3 Credits |
This course will prepare you to master technical, informational, and persuasive communication to meet organizational goals. Technical communication topics include a study of the nature, structure, and interpretation of data. Informational communication topics include data visualization and design of data for understanding and action. Persuasive communication topics include the study of written, verbal, and nonverbal approaches to influencing decision makers. | 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 700, DS 710 | 3 Credits |
Explore the current and future applications of data science as a strategic decision-making tool to achieve a competitive advantage in business. With an emphasis on obtaining decision-making value from an organization’s data assets, this course will investigate the use of data science findings to develop solutions to competitive business challenges. Through case studies, you will examine how data science methods can support business decision making, and discover a range of methods the data scientist can use to get people within the organization on board with data science projects. | 3 Credits |
Students will develop and execute a data science project using real-world data and communicate results to non-technical audiences. Prerequisites: DS 715, DS 730, DS 735, DS 745, DS 775 or DS 776 | 3 Credits |
Fall 2025
Registration Opens: April 14, 2025
Course Preview Week: August 26 - September 01, 2025
Semester Dates: September 02 - December 12, 2025
| Course | Credits |
|---|---|
This course provides an introduction to data science and highlights its importance in business decision making. It provides an overview of commonly used data science tools along with spreadsheets, relational databases, statistics, and programming assignments to lay the foundation for data science applications. | 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 | 3 Credits |
Computer programming is an essential part of data science. When working with large data sets, it’s especially important to be able to write effective, efficient code to help you organize and understand the data. In this course, we’ll introduce you to one of the most widely-used programming languages for data science: Python. You’ll gain experience working with real-world data, and leave the course with skills you can apply in other courses in the MS Data Science Program as well as on the job! | 3 Credits |
This course will introduce you to the concepts and techniques to work with and reason about subject-oriented, integrated, time-variant, and nonvolatile collections of data in support of management’s decision-making process. | 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 700, DS 710 | 3 Credits |
This course covers two aspects of data analytics. First, it teaches techniques to generate visualizations appropriate to the audience type, task, and data. Second, it teaches methods and techniques for analyzing unstructured data – including text mining, web text mining and social network analysis. Prerequisite: DS 740 | 3 Credits |
Investigate the ethical issues in computer science that ultimately also pertain to data science, including privacy, plagiarism, intellectual property rights, piracy, security, confidentiality, and many other issues. Your study of these issues will begin broadly, with a look at ethical issues in computer science at large. We will then make inferences to the narrower field of data science. We will consider ethical arguments and positions, the quality and integrity of decisions and inferences based on data, and how important cases and laws have shaped the legality, if not the morality, of data science-related computing. We will use case studies to investigate these issues. Prerequisite: DS 740 | 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 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. Prerequisite: 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: DS 715, DS 730, DS 735, DS 745, DS 775 or DS 776 | 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.
Experience UW Data Science
Learn about data science, meet the faculty, read student stories, and more. Read the blog.
