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UW–Eau Claire
UW-Green Bay
UW-La Crosse
UW–Oshkosh
UW-Stevens Point
UW-Superior

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 Extended Campus.


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.


UW System Collaboration

The Master of Science in Data Science is offered through UW Extended Campus in partnership with 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 an accredited university 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

All application materials need to be completed two weeks prior to the semester start 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. A nonrefundable $56 application fee is required for most graduate degree-seeking students applying to a UW System institution.*

*For a limited time, UW Extended Campus is offering an application fee waiver to those who haven’t yet applied to the Spring, Summer or Fall 2024 semesters.  To redeem, use coupon code APPLY24 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.

CourseCredits

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.

DS 700 Course Syllabus

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

DS 705 Course Syllabus

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!

DS 710 Syllabus

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.

DS 715 Syllabus

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

DS 730 Syllabus

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.

DS 735 Syllabus

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

DS 740 Syllabus

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

DS 745 Syllabus

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

DS 760 Syllabus

3 Credits

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

DS 775 Syllabus

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.

DS 780 Syllabus

3 Credits

This course describes the premise of the capstone—what it entails, its purpose, and an outline of work required to fulfill the capstone project requirements.

Students are provided with an overview of the capstone course objectives—how to prepare and organize for a semester-long project, the methods used to develop a project, descriptions of project options, and the supporting work that culminates in a final project.

This course provides the information and steps needed to select a topic and a format and then prepare the project proposal that is required in the second week of enrollment.

There are formal assignments within the capstone to keep you and the instructor aware of your progress. Students can contact the instructor if clarification is needed, questions arise, or there is an interest in project topic discussion and refinement.

Prerequisites: DS 715, DS 730, DS 735, DS 745, DS 775

Sample Capstone Projects

DS 785 Syllabus

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

Spring 2024

Course Preview Week: January 16 - January 22, 2024
Semester Dates: January 23 - May 03, 2024

CourseCredits

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.

DS 700 Course Syllabus

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

DS 705 Course Syllabus

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!

DS 710 Syllabus

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.

DS 715 Syllabus

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

DS 730 Syllabus

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.

DS 735 Syllabus

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

DS 740 Syllabus

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

DS 745 Syllabus

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

DS 760 Syllabus

3 Credits

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

DS 775 Syllabus

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.

DS 780 Syllabus

3 Credits

This course describes the premise of the capstone—what it entails, its purpose, and an outline of work required to fulfill the capstone project requirements.

Students are provided with an overview of the capstone course objectives—how to prepare and organize for a semester-long project, the methods used to develop a project, descriptions of project options, and the supporting work that culminates in a final project.

This course provides the information and steps needed to select a topic and a format and then prepare the project proposal that is required in the second week of enrollment.

There are formal assignments within the capstone to keep you and the instructor aware of your progress. Students can contact the instructor if clarification is needed, questions arise, or there is an interest in project topic discussion and refinement.

Prerequisites: DS 715, DS 730, DS 735, DS 745, DS 775

Sample Capstone Projects

DS 785 Syllabus

3 Credits

Summer 2024

Registration Opens: March 11, 2024
Course Preview Week: May 21 - May 27, 2024
Semester Dates: May 28 - August 09, 2024

CourseCredits

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!

DS 710 Syllabus

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.

DS 735 Syllabus

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

DS 740 Syllabus

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

DS 760 Syllabus

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.

DS 780 Syllabus

3 Credits

This course describes the premise of the capstone—what it entails, its purpose, and an outline of work required to fulfill the capstone project requirements.

Students are provided with an overview of the capstone course objectives—how to prepare and organize for a semester-long project, the methods used to develop a project, descriptions of project options, and the supporting work that culminates in a final project.

This course provides the information and steps needed to select a topic and a format and then prepare the project proposal that is required in the second week of enrollment.

There are formal assignments within the capstone to keep you and the instructor aware of your progress. Students can contact the instructor if clarification is needed, questions arise, or there is an interest in project topic discussion and refinement.

Prerequisites: DS 715, DS 730, DS 735, DS 745, DS 775

Sample Capstone Projects

DS 785 Syllabus

3 Credits

Fall 2024

Registration Opens: April 08, 2024
Course Preview Week: August 27 - September 02, 2024
Semester Dates: September 03 - December 13, 2024

CourseCredits

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.

DS 700 Course Syllabus

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

DS 705 Course Syllabus

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!

DS 710 Syllabus

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.

DS 715 Syllabus

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

DS 730 Syllabus

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

DS 740 Syllabus

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

DS 745 Syllabus

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

DS 760 Syllabus

3 Credits

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

DS 775 Syllabus

3 Credits

This course describes the premise of the capstone—what it entails, its purpose, and an outline of work required to fulfill the capstone project requirements.

Students are provided with an overview of the capstone course objectives—how to prepare and organize for a semester-long project, the methods used to develop a project, descriptions of project options, and the supporting work that culminates in a final project.

This course provides the information and steps needed to select a topic and a format and then prepare the project proposal that is required in the second week of enrollment.

There are formal assignments within the capstone to keep you and the instructor aware of your progress. Students can contact the instructor if clarification is needed, questions arise, or there is an interest in project topic discussion and refinement.

Prerequisites: DS 715, DS 730, DS 735, DS 745, DS 775

Sample Capstone Projects

DS 785 Syllabus

3 Credits

Spring 2025

Registration Opens: November 11, 2024
Course Preview Week: January 21 - January 27, 2025
Semester Dates: January 28 - May 09, 2025

CourseCredits

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.

DS 700 Course Syllabus

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!

DS 710 Syllabus

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.

DS 715 Syllabus

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

DS 730 Syllabus

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.

DS 735 Syllabus

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

DS 740 Syllabus

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

DS 745 Syllabus

3 Credits

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

DS 775 Syllabus

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.

DS 780 Syllabus

3 Credits

This course describes the premise of the capstone—what it entails, its purpose, and an outline of work required to fulfill the capstone project requirements.

Students are provided with an overview of the capstone course objectives—how to prepare and organize for a semester-long project, the methods used to develop a project, descriptions of project options, and the supporting work that culminates in a final project.

This course provides the information and steps needed to select a topic and a format and then prepare the project proposal that is required in the second week of enrollment.

There are formal assignments within the capstone to keep you and the instructor aware of your progress. Students can contact the instructor if clarification is needed, questions arise, or there is an interest in project topic discussion and refinement.

Prerequisites: DS 715, DS 730, DS 735, DS 745, DS 775

Sample Capstone Projects

DS 785 Syllabus

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 Extended Campus Grants and Scholarships

You may be eligible for a grant or scholarship as a student in a semester-based collaborative program through UW Extended Campus. 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.