The M.S. in Data Science program is overseen by a faculty oversight committee (FOC) led by a program director. Faculty from participating schools/colleges make up the oversight committee. The program consists of 30 credits, including 18 credits in six core areas and 12 credits of coursework in specialized skills in data science for specific applications in the field.

Program Type


Program Format

On Campus

The core areas include:

  1. Developing insights from data, for applications
  2. Organizing and maintaining large data sets.
  3. Methods like AI, and Machine Learning to extract insight from Data
  4. Knowledge and skills for using probabilistic methods to analyze uncertainty in data and develop insights
  5. Advanced Programming for Data Collection and Data Science
  6. Understand the importance of, and skills for, the ethical use of data

Twelve credits of coursework in specialized skills in data science for specific applications and fields provide students with the opportunity to choose and pursue electives related to their primary discipline of interest via courses offered in multiple disciplines including anthropology, business, biological sciences, computer science, geography and sociology among others.

Student Learning Outcomes and Program Objectives

The core objective of the M.S. in Data Science is to prepare students to pursue a data science oriented career path in the discipline that they are passionate about. The program is designed to allow students to progress through the areas mentioned above. Graduates of the M.S. in Data Science will be prepared to apply the concepts of data science inter-disciplinarily to problems in a variety of fields and industries and appreciate and abide by ethical uses of data and insights from the use of data science. Students will:

  1. Develop insights from data, for applications,
  2. Learn how to work with large data sets,
  3. Gain experience in advanced computer programming for data science
  4. Become skilled in specific areas of data science such as artificial intelligence and machine learning
  5. Understand how to deal with uncertainty which is an inherent characteristic of data science and

Recognize and internalize the importance of ethical use of data and data science.

Program Requirements and Curriculum

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 UW-MILWAUKEE Center for 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. 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 if they have 6 credits or fewer of the proficiency requirements remaining to be completed. Coursework taken to meet the  proficiency requirements does not count towards the degree requirements.

The table below illustrates the program curriculum for the proposed program. The program requirements are comprised of 30 credits, of which there are 18 credits across the six core areas, 12 credits of general electives in the seventh area for Specialized Skills in Data Science for Specific Applications and Fields of which 3 credits may be fulfilled with a capstone course. Enrollment in an internship or thesis is subject to the approval of the Program Director and the signature of a faculty member willing to guide the thesis or internship. Every student’s program of electives must be approved by the program director. Subject to approval, students may be able to count as electives some courses in the “core” categories not applied to the core requirements. Students wishing to apply other courses not listed above towards the electives requirements must have each course approved by the program director.

Core Coursework (18 credits)

Developing insights from data for application (choose one of the following)

ATM SCI 600     Data Analytics3
INFOST 687      Data Analysis for Data Science3
COMPSCI 425(G) Introduction to Data Mining3

Organizing and maintaining large data sets (choose one of the following)

INFOST 785       Database Management systems for information professionals3
INFOST 714      Metadata3
INFOST 780      XML for Libraries3
INFOST 783      Information Storage and Retrieval3
INFOST 691      Data Management and Curation3
COMPSCI 557   Database Systems3
PH 718             Data Management and Visualization in R 3
BUS ADM 749   Data and Information Management3

AI, and Machine Learning to extract insight from Data (choose one of the following)

INFOST 582      Introduction to Data Science3
BUS ADM 795   Seminar-in-Management: Ideas & Applications of Data Science In
  Different Fields
COMPSCI 422G Introduction to Artificial Intelligence3
COMPSCI 710   Artificial Intelligence3
COMPSCI 411G Machine Learning and Applications3
COMPSCI 711   Introduction to Machine Learning3
MATH 702        Industrial MATH 23

Probabilistic methods to analyze uncertainty in data (choose one of the following)

ATM SCI 500     Statistical Methods in Atmospheric Sciences3
ATM SCI 700     Statistical Methods in Atmospheric Sciences II: Signal Detection3
BUS ADM 754   Statistical Analysis3
BUSMGMT 709 Predictive Analytics for Managers3
BUS ADM 713   Business Forecasting Methods3
BUS ADM 714   Multivariate Techniques in Mgmt Research3
IND ENG 575    Design of Experiments3
IND ENG 765    Operations Research Methods3
SOCIOL 461G   Social Data Analysis Using Regression3
SOCIOL 760      Advanced Statistical Methods in Sociology3
SOCIOL 982      Advanced Quantitative Analysis3
PSYCH 510G     Advanced Psychological Statistics3
PSYCH 610G     Experimental Design3
POL SCI 390G   Political Data Analysis3
POL SCI 701     Techniques of Political Science Research3
POL SCI 702     Advanced Techniques of Political Science Research3
ECON 411G      Economic Forecasting Methods3
ECON 413G      Statistics for Economists3
ECON 513G      Introduction to Econometrics3
GEOG 747        Spatial Analysis3
PH 711             Intermediate Biostatistics3
PH 818             “Statistical Computing (“”This course will cover the theory and                         application of common algorithms used in statistical computing.)”3
GEOG 827        Qualitative Research3
COMPST 701    Mathematical & Computing Fundamentals for IT Professionals3
MTHSTAT 361G Intro Prob/Stats I3
MTHSTAT 362G Intro Prob/Stats II3
MTHSTAT 763   Regression3
MTHSTAT 764   Time Series Analysis3
MATH 571G      Probability Models3
COMPSCI 720   Computational models for decision making3
MTHSTAT 761  Mathematical Statistics I3
MTHSTAT 762  Mathematical Statistics II3
ED PSY 724       Educational Statistical Methods II3
ED PSY 820       Multiple Regression and Other General Linear Models3

Advanced Programming for Data Collection and Data Science (choose one of the following)

BUSMGMT 744 R Programming for Business Analytics3
COMPST 702    Software Development in Python3
GEOG 748        ArcGIS Programming with Python3
URBPLAN 794  Internet Geographic Information Systems3
COMPST 751    Data Structures and Algorithms3
MTHSTAT 766  Computational Statistics3

Ethics (choose one of the following)

INFOST 660       Information Policy3
INFOST 661       Information Ethics3
INFOST 583       Survey of Information Security3
INFOST 784       Information Security Management3
INFOST 761       Information Privacy3
INFOST 465G    Legal aspects of info products & services (G)3
BUS ADM 743   Information Privacy, Security, and Continuity3

Specialized skills in data science for specific application in the field (12 credits)

Choose from the following:

INFOST 691      Artificial Intelligence and Disruptive Technologies3
BUS ADM 741   Web Mining and Analytics3
BUS ADM 812   Machine Learning for Business.3
BUS ADM 813   Social Media Analytics for Business3
BUS ADM 817   Connected Systems for Business3
BUS ADM 742   Big Data in Business3
BUS ADM 745   Artificial Intelligence for Business3
BUS ADM 763   Marketing Analytics3
BUS ADM 769   Database Marketing3
BUS ADM 816   Business Intelligence Technologies & Solutions3
COMPSCI 712   Image Processing3
COMPSCI 423G Natural Language Processing3
COMPSCI 723   Natural Language Processing3
COMPSCI 744   Text Retrieval3
COMPSCI 469G Security3
COMPSCI 535G Analysis of Algorithms3
COMPSCI 704   Analysis of Algorithms3
COMPSCI 759   Data Security3
Comp Sci 725   Robot Motion Planning3
Comp Sci 755   Information and Coding Theory3
SOCIOL. 750     Research Methods in Sociology3
SOCIOL. 752     Fundamentals of Survey Methodology3
SOCIOL 952      Social Network Analysis3
POL SCI392G    Survey Research3
GEOG 704        Remote Sensing: Environmental and Land Use Analysis3
GEOG 705        Cartography3
GEOG 716        Watershed Analysis and Modeling3
GEOG 726        Geographic Information Science3
GEOG 804        Advanced Remote Sensing3
GEOG 826        Intermediate Geographic Information Science3
GEOG 834        GIS and Society3
GEOG 904        Remote Sensing and Urban Analysis3
GEOG 926        Advanced Geographic Information Science: Geographic Modeling3
GEOG 960        Seminar: Geographic Techniques3
GEOG 999        Independent Work (with appropriate topic)3
URBPLAN 692  Special Topics in Urban Planning: Transportation Planning and GIS3
PH 812             Statistical Learning and Data Mining3
URBPLAN 791  Introduction to Urban Geographic Information Systems for Planning3
URBPLAN 792  Using Urban Geographic Information Systems for Planning3
URBPLAN 999  Independent Study3
ANTHRO 380    Anthropological Applications of GIS3
ANTHRO 562    Techniques and Problems in Archaeology3
ANTHO 768      Topics in Advanced Research Design in Anthropology3
CRM JST 520G  Analysis Oriented Technology: Spatial Data Analysis; Crime Mapping; ArcGIS3
CRM JST 713     Measuring Crime & Analyzing Crime Data3
CRM JST 716     Advanced Analytic Techniques for Crime Analysts3
CRM JST 910     Methods and Practice Capstone for Crime Analysts3
ART 526G         Research in Universal Design and Fabrication3
ART 316 G        Interactive and Multimedia Art3
ART 317 G        3D Imaging I3
ART 427 U/G    Special Topics Course3
ART 313 U/G    Interactive and Multimedia Art and Programming for Artists3
ED PSY 720       Techniques of Educational and Psychological Measurement3
MATH 701        Industrial MATH 13
ED PSY 821       Psychometric Theory and Practice3
ED PSY 822       Modern Test Theory3
ED PSY 823       Structural Equation Modeling3
ED PSY 824       Advanced Experimental Design and Analysis3
ED PSY 825       Multivariate Methods3
ED PSY 826       Analysis of Cross-Classified Categorical Data3
ED PSY 827       Survey Research Methods in Education3
ED PSY 832       Theory of Hierarchical Linear Modeling3
BIO SCI 469      Genomic Data Analysis3
BIO SCI 502      Introduction to Programming and Modeling in Ecology and Evolution3
BIO SCI 572      Functional Genomics3

Optional: Internship / Thesis / Capstone

Of the required 12 elective credits, up to three 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 below or enroll for thesis/internship credits with their thesis advisor (in the advisor’s department).

Course NumberCourse Name
INFOST 790 Project Design, Implementation, and Evaluation
GEOG 798  GIS/Cartography Internship
URBPLAN 793Applied Projects in Urban Geographic Information Systems
URBPLAN 991Legislative/Administrative Agency Internship
MATH 790 Master’s Thesis
COMPSCI 990   Master’s Thesis
COMPSCI 995 Master’s Capstone Project

Qualifying Exam

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 will have seven days to complete it.

The program requires completion of 30 credits. A full-time student would typically complete the requirements in two years or four semesters. A part-time student enrolling in one course per semester would require 10 semesters to complete the program.