Data Science MS
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
Master’s
Program Format
On Campus, Online
The core areas include:
- Developing insights from data, for applications
- Organizing and maintaining large data sets.
- Methods like AI, and Machine Learning to extract insight from Data
- Knowledge and skills for using probabilistic methods to analyze uncertainty in data and develop insights
- Advanced Programming for Data Collection and Data Science
- 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:
- Develop insights from data, for applications,
- Learn how to work with large data sets,
- Gain experience in advanced computer programming for data science
- Become skilled in specific areas of data science such as artificial intelligence and machine learning
- 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)
| Course | Credits |
|---|---|
| ATM SCI 600 Data Analytics | 3 |
| INFOST 687 Data Analysis for Data Science | 3 |
| COMPSCI 425(G) Introduction to Data Mining | 3 |
Organizing and maintaining large data sets (choose one of the following)
| Course | Credits |
|---|---|
| INFOST 785 Database Management systems for information professionals | 3 |
| INFOST 714 Metadata | 3 |
| INFOST 780 XML for Libraries | 3 |
| INFOST 783 Information Storage and Retrieval | 3 |
| INFOST 691 Data Management and Curation | 3 |
| COMPSCI 557 Database Systems | 3 |
| PH 718 Data Management and Visualization in R | 3 |
| BUS ADM 749 Data and Information Management | 3 |
AI, and Machine Learning to extract insight from Data (choose one of the following)
| Course | Credits |
|---|---|
| INFOST 582 Introduction to Data Science | 3 |
| BUS ADM 795 Seminar-in-Management: Ideas & Applications of Data Science In Different Fields | 3 |
| COMPSCI 422G Introduction to Artificial Intelligence | 3 |
| COMPSCI 710 Artificial Intelligence | 3 |
| COMPSCI 411G Machine Learning and Applications | 3 |
| COMPSCI 711 Introduction to Machine Learning | 3 |
| MATH 702 Industrial MATH 2 | 3 |
Probabilistic methods to analyze uncertainty in data (choose one of the following)
| Course | Credits |
|---|---|
| ATM SCI 500 Statistical Methods in Atmospheric Sciences | 3 |
| ATM SCI 700 Statistical Methods in Atmospheric Sciences II: Signal Detection | 3 |
| BUS ADM 754 Statistical Analysis | 3 |
| BUSMGMT 709 Predictive Analytics for Managers | 3 |
| BUS ADM 713 Business Forecasting Methods | 3 |
| BUS ADM 714 Multivariate Techniques in Mgmt Research | 3 |
| IND ENG 575 Design of Experiments | 3 |
| IND ENG 765 Operations Research Methods | 3 |
| SOCIOL 461G Social Data Analysis Using Regression | 3 |
| SOCIOL 760 Advanced Statistical Methods in Sociology | 3 |
| SOCIOL 982 Advanced Quantitative Analysis | 3 |
| PSYCH 510G Advanced Psychological Statistics | 3 |
| PSYCH 610G Experimental Design | 3 |
| POL SCI 390G Political Data Analysis | 3 |
| POL SCI 701 Techniques of Political Science Research | 3 |
| POL SCI 702 Advanced Techniques of Political Science Research | 3 |
| ECON 411G Economic Forecasting Methods | 3 |
| ECON 413G Statistics for Economists | 3 |
| ECON 513G Introduction to Econometrics | 3 |
| GEOG 747 Spatial Analysis | 3 |
| PH 711 Intermediate Biostatistics | 3 |
| PH 818 “Statistical Computing (“”This course will cover the theory and application of common algorithms used in statistical computing.)” | 3 |
| GEOG 827 Qualitative Research | 3 |
| COMPST 701 Mathematical & Computing Fundamentals for IT Professionals | 3 |
| MTHSTAT 361G Intro Prob/Stats I | 3 |
| MTHSTAT 362G Intro Prob/Stats II | 3 |
| MTHSTAT 763 Regression | 3 |
| MTHSTAT 764 Time Series Analysis | 3 |
| MATH 571G Probability Models | 3 |
| COMPSCI 720 Computational models for decision making | 3 |
| MTHSTAT 761 Mathematical Statistics I | 3 |
| MTHSTAT 762 Mathematical Statistics II | 3 |
| ED PSY 724 Educational Statistical Methods II | 3 |
| ED PSY 820 Multiple Regression and Other General Linear Models | 3 |
Advanced Programming for Data Collection and Data Science (choose one of the following)
| Course | Credits |
|---|---|
| BUSMGMT 744 R Programming for Business Analytics | 3 |
| COMPST 702 Software Development in Python | 3 |
| GEOG 748 ArcGIS Programming with Python | 3 |
| URBPLAN 794 Internet Geographic Information Systems | 3 |
| COMPST 751 Data Structures and Algorithms | 3 |
| MTHSTAT 766 Computational Statistics | 3 |
Ethics (choose one of the following)
| Course | Credits |
|---|---|
| INFOST 660 Information Policy | 3 |
| INFOST 661 Information Ethics | 3 |
| INFOST 583 Survey of Information Security | 3 |
| INFOST 784 Information Security Management | 3 |
| INFOST 761 Information Privacy | 3 |
| INFOST 465G Legal aspects of info products & services (G) | 3 |
| BUS ADM 743 Information Privacy, Security, and Continuity | 3 |
Specialized skills in data science for specific application in the field (12 credits)
Choose from the following:
| Course | Credits |
|---|---|
| INFOST 691 Artificial Intelligence and Disruptive Technologies | 3 |
| BUS ADM 741 Web Mining and Analytics | 3 |
| BUS ADM 812 Machine Learning for Business. | 3 |
| BUS ADM 813 Social Media Analytics for Business | 3 |
| BUS ADM 817 Connected Systems for Business | 3 |
| BUS ADM 742 Big Data in Business | 3 |
| BUS ADM 745 Artificial Intelligence for Business | 3 |
| BUS ADM 763 Marketing Analytics | 3 |
| BUS ADM 769 Database Marketing | 3 |
| BUS ADM 816 Business Intelligence Technologies & Solutions | 3 |
| COMPSCI 712 Image Processing | 3 |
| COMPSCI 423G Natural Language Processing | 3 |
| COMPSCI 723 Natural Language Processing | 3 |
| COMPSCI 744 Text Retrieval | 3 |
| COMPSCI 469G Security | 3 |
| COMPSCI 535G Analysis of Algorithms | 3 |
| COMPSCI 704 Analysis of Algorithms | 3 |
| COMPSCI 759 Data Security | 3 |
| Comp Sci 725 Robot Motion Planning | 3 |
| Comp Sci 755 Information and Coding Theory | 3 |
| SOCIOL. 750 Research Methods in Sociology | 3 |
| SOCIOL. 752 Fundamentals of Survey Methodology | 3 |
| SOCIOL 952 Social Network Analysis | 3 |
| POL SCI392G Survey Research | 3 |
| GEOG 704 Remote Sensing: Environmental and Land Use Analysis | 3 |
| GEOG 705 Cartography | 3 |
| GEOG 716 Watershed Analysis and Modeling | 3 |
| GEOG 726 Geographic Information Science | 3 |
| GEOG 804 Advanced Remote Sensing | 3 |
| GEOG 826 Intermediate Geographic Information Science | 3 |
| GEOG 834 GIS and Society | 3 |
| GEOG 904 Remote Sensing and Urban Analysis | 3 |
| GEOG 926 Advanced Geographic Information Science: Geographic Modeling | 3 |
| GEOG 960 Seminar: Geographic Techniques | 3 |
| GEOG 999 Independent Work (with appropriate topic) | 3 |
| URBPLAN 692 Special Topics in Urban Planning: Transportation Planning and GIS | 3 |
| PH 812 Statistical Learning and Data Mining | 3 |
| URBPLAN 791 Introduction to Urban Geographic Information Systems for Planning | 3 |
| URBPLAN 792 Using Urban Geographic Information Systems for Planning | 3 |
| URBPLAN 999 Independent Study | 3 |
| ANTHRO 380 Anthropological Applications of GIS | 3 |
| ANTHRO 562 Techniques and Problems in Archaeology | 3 |
| ANTHO 768 Topics in Advanced Research Design in Anthropology | 3 |
| CRM JST 520G Analysis Oriented Technology: Spatial Data Analysis; Crime Mapping; ArcGIS | 3 |
| CRM JST 713 Measuring Crime & Analyzing Crime Data | 3 |
| CRM JST 716 Advanced Analytic Techniques for Crime Analysts | 3 |
| CRM JST 910 Methods and Practice Capstone for Crime Analysts | 3 |
| ART 526G Research in Universal Design and Fabrication | 3 |
| ART 316 G Interactive and Multimedia Art | 3 |
| ART 317 G 3D Imaging I | 3 |
| ART 427 U/G Special Topics Course | 3 |
| ART 313 U/G Interactive and Multimedia Art and Programming for Artists | 3 |
| ED PSY 720 Techniques of Educational and Psychological Measurement | 3 |
| MATH 701 Industrial MATH 1 | 3 |
| ED PSY 821 Psychometric Theory and Practice | 3 |
| ED PSY 822 Modern Test Theory | 3 |
| ED PSY 823 Structural Equation Modeling | 3 |
| ED PSY 824 Advanced Experimental Design and Analysis | 3 |
| ED PSY 825 Multivariate Methods | 3 |
| ED PSY 826 Analysis of Cross-Classified Categorical Data | 3 |
| ED PSY 827 Survey Research Methods in Education | 3 |
| ED PSY 832 Theory of Hierarchical Linear Modeling | 3 |
| BIO SCI 469 Genomic Data Analysis | 3 |
| BIO SCI 502 Introduction to Programming and Modeling in Ecology and Evolution | 3 |
| BIO SCI 572 Functional Genomics | 3 |
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 Number | Course Name |
|---|---|
| INFOST 790 | Project Design, Implementation, and Evaluation |
| GEOG 798 | GIS/Cartography Internship |
| URBPLAN 793 | Applied Projects in Urban Geographic Information Systems |
| URBPLAN 991 | Legislative/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.