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Data Science


Erik Krohn, Program Coordinator
Office: S. Halsey S216
Telephone: (877) 895-3276
Web Site:


The Master of Science in Data Science is a collaboration between UW-Extension and six UW System campuses—UW-Eau Claire, UW-Green Bay, UW-La Crosse, UW-Oshkosh, UW-Stevens Point, and UW-Superior. All courses in this online program are developed and taught by University of Wisconsin Data Science faculty—the same faculty who teach our on-campus courses.


The Masters of Science in Data Science is offered cooperatively with 5 other UW campuses and administered in part by UW Extension. This program is designed to prepare data science professionals to solve real-world problems as part of an interdisciplinary team using structured and unstructured data.


Completion of the program will lead to the degree: Master of Science (MS)


In addition to the requirements of the Office of Graduate Studies specified in the POLICIES section of this Bulletin, the program has established additional policies and procedures for admission.

  • 3.0 GPA
  • Recent prerequisite coursework in:
    • Elementary Statistics
    • Introduction to Programming
    • Introduction to Databases

(Students will be required to satisfy all program prerequisites prior to formal admission into the program.)

  • Two letters of recommendation
  • Resume
  • Up to 1000-word statement of personal intent describing the decision to pursue the degree and what you believe you will bring to the data science field.


A. Structure
The program is comprised of 12 core courses ranging from data mining to high performance computing to strategic decision making. Because the program is entirely online, you can study and do homework whenever you like, wherever you have an Internet connection. Courses have no set meeting times and you never need to come to campus. An innovative Virtual Lab lets you remotely access software tools and programming languages such as R, Python, SQL Server, and Tableau, saving you the cost, time, and hassle of purchasing and installing these applications on your own computer.

B. Academic Plans of Study
Data Science is the description for the Data Science plan of study.

C. Minimum Credit Requirements
36 credits applicable to the graduate degree constitute the minimal requirement for all students seeking the MS.

D. Admission to Candidacy
Students must satisfy fully the Office of Graduate Studies requirements for advancement to candidacy as stated in the POLICIES section of this Bulletin. Students must confer with their program coordinator/advisor to plan and receive program approval for their admission to candidacy. Students should apply for Admission to Candidacy after completing 9-21 credits. The Office of Graduate Studies gives final approval to Admission to Candidacy.


Core Courses: 36 credits

Data Science

700 3 Foundations of Data Science
705 3 Statistical Methods
710 3 Programming for Data Science
715 3 Data Warehousing
730 3 Big Data: High Performance Computing
735 3 Communicating about Data
740 3 Data Mining
745 3 Visualization and Unstructured Data Analysis
760 3 Ethics of Data Science
775 3 Prescriptive Analytics
780 3 Data Science and Strategic Decision Making
785 3 Capstone


MS in Data Science 700

3 (crs.)

Foundations of Data Science

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.

MS in Data Science 705

3 (crs.)

Statistical Methods

Statistical methods and inference procedures will be presented in this course with an emphasis on applications, computer implementation, and interpretation of results. Topics include simple and multiple regression, model selection, correlation, moderation/interaction analysis, logistic regression, chi-square test, ANOVA, Kruskal-Wallis test, MANOVA, factor analysis, and canonical correlation analysis.

MS in Data Science 710

3 (crs.)

Programming for Data Science

Introduction to programming languages and packages used in Data Science.

MS in Data Science 715

3 (crs.)

Data Warehousing

Introduce 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.

MS in Data Science 730

3 (crs.)

Big Data High Performance Computing

This course will teach students how to process large data sets efficiently. Students will be introduced to non-relational databases. Students will learn algorithms that allow for the distributed processing of large data sets across clusters. Prerequisite: Data Science 710

MS in Data Science 735

3 (crs.)

Communicating About Data

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.

MS in Data Science 740

3 (crs.)

Data Mining

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: Data Science 705 and 710

MS in Data Science 745

3 (crs.)

Visualization and Unstructured Data Analysis

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. Prerequisites: Data Science 700, 705, 710, and 740.

MS in Data Science 760

3 (crs.)

Ethics of Data Science

This course will focus on the investigation of ethical issues in computer science that ultimately also pertain to data science, including privacy, plagiarism, intellectual property rights, piracy, security, confidentiality and many others issues. Our 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. Case studies will be used to investigate issues. Prerequisites: Data Science 700 or 780.

MS in Data Science 775

3 (crs.)

Prescriptive Analytics

This course covers procedures and techniques for using data to inform the decision-making process. Topics include optimization, decision analysis, game theory, and simulation. Case studies and applications will be emphasized. Prerequisites: Data Science 705.

MS in Data Science 780

3 (crs.)

Data Science and Strategic Decision Making

The interaction between data science and strategic decision making. Leveraging data resources for competitive advantage in the marketplace. This course examines how data science relates to developing strategies for business organizations. The emphasis is on obtaining decision-making value from an organization’s data assets. The course will investigate the use of data science findings to develop solutions to competitive business challenges. Case studies will be reviewed to examine how data science methods can support business decision-making. A range of methods the data scientist can use to get people within the organization on-board with data science projects will be reviewed. The future of data science as a decision-making tool will be explored.

MS in Data Science 785

3 (crs.)


Capstone course in which students will develop and execute a project involving real-world data. Projects will include: formulation of a question to be answered by the data; collection, cleaning and processing of data; choosing and applying a suitable model and/or analytic method to the problem; and communicating the results to a non-technical audience. Prerequisites: Data Science 700, 705, 710, 715, 730, 735, 740, 745, and 775.