Data Everywhere

There has been an explosion of data over the last decade. Everything that people do like listening to music, streaming shows, using social media or rideshares generates data. In fact, almost everything that goes on in the world today is measured and recorded somewhere. Analyzing that data can vastly improve human lives and business performance. So, it’s not surprising that analytics are now used routinely even in fields that did not use much data before like the Arts, Music, and Creative Writing. Analytics are also extensively deployed in Business, Engineering and Manufacturing and Government and even in many not-for-profit sectors like education, fundraising and social welfare. It is safe to say that virtually all human activity that affects our lives uses analytics in some way today. Visit this link for more information on how UWM prepares students for careers in data science fields.

Why should you Consider a Master of Science in Data Science (MSDS)?

With every field turning to data to improve decision-making and performance, Data Science is one of the fastest growing professions today but there aren’t enough trained data analysts to fill that need. A Master’s degree in Data Science that trains you to analyze data can therefore help you in finding jobs with attractive salaries. 

A report from the employment outlook firm Burning Glass produced jointly with IBM and the Business Higher Education Forum identified several job categories in the data science and analytics field, including data driven decision makers (“leverage data to inform strategic and operational decisions”) and functional analysts (“utilize data and analytical models to inform specific functions and business decisions”). They estimated a national demand of 1.8 million job postings nationwide for 2020, with a 5-year growth rate of approximately 15%. Importantly, the report also states: “39% of Data Scientists and Advanced Analysts require a Master’s or Ph.D. These degrees take additional years of schooling to complete, so it will take a significant time investment to train a larger pool of workers. Therefore, because these roles are already undersupplied and projected to grow rapidly, the skills shortage is in danger of worsening.”

The Bureau of Labor Statistics also projects that Computer and Information Research Scientists category of jobs will grow 15% over the 2019-2029 period and describes this as: “…much faster than average for all occupations[1]. Job prospects are expected to be excellent” and states that the “median annual wage for computer and information research scientists was $126,830 in May 2020.” BLS also classifies this as a category in which most jobs require a master’s degree.

Additional evidence of demand is also seen in investments made by employers like Northwestern Mutual that have invested significant resources of $15 million in the establishment of the Northwestern Mutual Data Science Institute to support the launch and growth of undergraduate and graduate programs related to data including data science and data analytics.

Flexible Curriculum

We understand the needs of working professionals and offer an in-person or online program that will both build your career and enhance your professional networking opportunities. Fulfill your professional goals through a wide selection of courses offered in on-campus, online, and hybrid delivery options. 

MS Data Science Courses

To see a list of current classes available for the MS Data Science degree please see our MS Data Science Current Classes page.

For a complete listing of ALL classes related to the degree please see the Requirements tab.

Why UWM?

The MSDS at UWM is unique because its goal is to train graduates to practice data analytics in a field they are most passionate about. For example, if your interest is healthcare, you can become a data analyst in healthcare. If your passion is education, you can get the training to become an analyst in the field of education. The MSDS is therefore designed to give you the flexibility to build a career in data science in whatever field you want. 

Ready to Apply? 

The Data Science program is a multidisciplinary program. To apply, visit this link and click on the Apply Now tab to find the MS Data Science degree under Multidisciplinary Programs. Or go directly to the MS Data Science application.


[1] Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, Computer and Information Research Scientists, at (visited January 04, 2022)

Admission Requirements

Application Deadlines

Application deadlines vary by program, please review the application deadline chart for specific programs. Other important dates and deadlines can be found by using the One Stop calendars.


For admission to the M.S. in Data Science program, students must meet the general requirements of admission to a graduate program at UW-Milwaukee. As stated by the Graduate School, these requirements include: (1) a baccalaureate degree, or its equivalent as determined by the UWM Center on International Education, from a regionally accredited institution, completed before the first term of enrollment in the Graduate School; (2) Proficiency in the English language; and (3) A minimum cumulative undergraduate grade point average (GPA) of 2.75 on a 4.0 scale, or an equivalent measure on a grading system that does not use a 4.0 scale. Applications must include a reason statement, at least one letter of recommendation, and other materials as specified in the graduate application system. Incomplete applications will not be considered.

Students applying to the program are expected to have proficiency, demonstrated through coursework, exams or a portfolio, in the following areas: Linear Algebra (3 credits), Multivariable Calculus (4 credits), Statistics (3 credits), and Computer Literacy (6 credits). Those without these proficiencies may be admitted when they have 6 credits or fewer of the proficiency requirements remaining to be completed, but proficiency coursework does not count towards the MS.

Ready to Apply? 

The Data Science program is a multidisciplinary program. To apply go to: and click on the Apply Now tab to find the MS Data Science degree under Multidisciplinary Programs. Or go directly to the MS Data Science application.

Credits and Courses

Core Areas
Complete 18 credits; 1 course from each sub-area below:
Developing insights from data for applications3
Complete 1 course:
Data Analytics
Introduction to Data Mining
Introduction to Data Science
Organizing and maintaining large data sets3
Complete 1 course:
Data and Information Management
Introduction to Database Systems
Special Topics in Information Science: (Topic: Data Management and Curation)
Database Managment Systems for Information Professionals
Data Management and Visualization in R
AI and Machine Learning to extract insight from Data3
Complete 1 course:
Ideas and Applications of Data Science in Different Fields
Machine Learning and Applications
Introduction to Artificial Intelligence
Artificial Intelligence
Introduction to Machine Learning
Applied Econometrics
Data Analysis for Data Science
Industrial Mathematics II
Probabilistic methods to analyze uncertainty in data3
Complete 1 course:
Statistical Methods in Atmospheric Sciences
Statistical Methods in Atmospheric Sciences II: Signal Detection
Statistical Analysis
Business Forecasting Methods
Multivariate Techniques in Management Research
Predictive Analytics for Managers
Computational Models of Decision Making
Computing Fundamentals for IT Professionals
Economic Forecasting Methods
Statistics for Economists
Introduction to Econometrics
Econometric Methods II
Educational Statistical Methods II
Multiple Regression
Qualitative Research
Spatial Analysis
Design of Experiments
Operations Research Methods
Introduction to Probability Models
Introduction to Mathematical Statistics I
Introduction to Mathematical Statistics II
Regression Analysis
Regression Analysis
Time Series Analysis
Time Series Analysis
Mathematical Statistics I
Mathematical Statistics II
Introduction to Biostatistics
Intermediate Biostatistics
Statistical Computing
Techniques of Political Science Research
Advanced Techniques of Political Science Research
Advanced Psychological Statistics
Experimental Design
Social Data Analysis Using Regression
Advanced Statistical Methods in Sociology
Advanced Quantitative Analysis
Advanced Programming for Data Collection and Data Science3
Complete 1 course:
R Programming for Business Analytics
Introductory Programming Using Python
Data Structures and Algorithms
Computational Statistics
Computational Statistics
Internet Geographic Information Systems (GIS)
Ethics and Society3
Complete 1 course:
Information Privacy, Security & Continuity
Legal Aspects of Information Products and Services
Survey of Information Security
Information Policy
Information Ethics
Information Security Management
Electives 112
Complete 4 courses:
Anthropological Applications of GIS
Techniques and Problems in Archaeology
Topics in Advanced Research Design in Anthropology
Creative Coding
Creative Interfaces
3D Environments and XR
Advanced Design Workshop:
Research in Universal Design and Fabrication
Web Mining and Analytics
Big Data in Business
Artificial Intelligence for Business
Marketing Analytics
Database Marketing
Machine Learning for Business
Social Media Analytics for Business
Business Intelligence Technologies & Solutions
Connected Systems for Business
Introduction to Natural Language Processing
Natural Language Processing
Introduction to Text Retrieval and Its Applications in Biomedicine
Text Retrieval and Its Applications in Biomedicine
Introduction to Computer Security
Algorithm Design and Analysis
Analysis of Algorithms
Image Processing
Robot Motion Planning
Information and Coding Theory
Data Security
Advanced Machine Learning
Analysis Oriented Technology: Spatial Data Analysis; Crime Mapping; ArcGIS
Measuring Crime & Analyzing Crime Data
Advanced Analytic Techniques for Crime Analysts
Methods and Practice Capstone for Crime Analysts
Foundation of Econometric Methods
Econometric Methods I
Psychometric Theory and Practice
Item Response Theory
Structural Equation Modeling
Advanced Experimental Design and Analysis
Multivariate Methods
Analysis of Cross-Classified Categorical Data
Survey Research Methods
Theory of Hierarchical Linear Modeling
Remote Sensing: Environmental and Land Use Analysis
Watershed Analysis and Modeling
Geographic Information Science
Advanced Remote Sensing
Intermediate Geographic Information Science
Survey of Web and Mobile Content Development
Applied Web 3.0: Artificial Intelligence and Blockchain
Ethical Hacking I
Ethical Hacking II
Data Curation
Industrial Mathematics I
Statistical Learning & Data Mining
Political Data Analysis
Survey Research
Research Methods in Sociology
Fundamentals of Survey Methodology
Social Network Analysis
Special Topics in Urban Planning: (Topic: Transportation Planning and GIS)
Introduction to Urban Geographic Information Systems for Planning
Using Urban Geographic Information Systems (GIS) for Planning
Optional: Internship/Thesis Capstone 2
Reading and Research
Masters Thesis
Master's Capstone Project
Research Seminar for M.A. Students
Research or Thesis
Independent Reading
GIS/Cartography Internship
Research and Master Thesis
Independent Work
Information Science and Technology Independent Study
Fieldwork in Information Science and Technology
Master's Thesis
Industrial Internship
Applied Projects in Urban Geographic Information Systems
Legislative/Administrative Agency Internship
Independent Study
Qualifying Exam 3
Total Credits30

INFOST 691 (Topic: Artificial Intelligence and Disruptive Technologies) may also be used as an elective. Every student’s program of electives must be approved by the program director; students may be able to count as Electives some courses in the “core” categories not applied to the core requirements (subject to Director’s approval). Students wishing to apply other courses not listed here towards these electives must have each course approved by the program director.


Of the required 12 elective credits, up to 3 degree credits may be awarded for a thesis or internship. Students who choose this option must complete a relevant thesis or internship that is approved by the program director. Students who choose to complete a thesis must work with a thesis advisor and have the thesis approved by the advisor and the program director. Students who choose to pursue an internship must also obtain approval from the program director. Students may select from courses such as those listed in the table or enroll for thesis credits with their thesis advisor (in the advisor’s department).


Students who do not choose to pursue the optional capstone course/thesis/internship option are required to pass a qualifying exam. During this exam, students are given a data set and a research problem to be addressed with the data, using data science techniques. Students must submit a final report in which they use the provided data set to address the research question and demonstrate that they have developed a sufficient level of expertise to work as a data scientist. This is a take-home exam and students have seven days to complete it.

Additional Requirements

Major Professor as Advisor

Admitted students are assigned a faculty advisor who will work with the student to assemble a program of study. 

Time Limit

The student must complete all degree requirements within five years of initial enrollment.