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. For more information about the M.S. in Data Science program please attend one of our upcoming Information Sessions.
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 https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm (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.
Admission
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: graduateschool-apply.uwm.edu 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
Code | Title | Credits |
---|---|---|
Core Areas | ||
Complete 18 credits; 1 course from each sub-area below: | ||
Developing insights from data for applications | 3 | |
Complete 1 course: | ||
Data Analytics | ||
Introduction to Data Mining | ||
Introduction to Data Science | ||
Organizing and maintaining large data sets | 3 | |
Complete 1 course: | ||
Data and Information Management | ||
Introduction to Database Systems | ||
Special Topics in Information Science: (Topic: Data Management and Curation) | ||
Metadata | ||
Database Managment Systems for Information Professionals | ||
Data Management and Visualization in R | ||
AI and Machine Learning to extract insight from Data | 3 | |
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 data | 3 | |
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 | ||
or ECON 703 | 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 | ||
or MTHSTAT 763 | Regression Analysis | |
Time Series Analysis | ||
or MTHSTAT 764 | 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 Science | 3 | |
Complete 1 course: | ||
R Programming for Business Analytics | ||
Introductory Programming Using Python | ||
Data Structures and Algorithms | ||
Computational Statistics | ||
or MTHSTAT 766 | Computational Statistics | |
Internet Geographic Information Systems (GIS) | ||
Ethics and Society | 3 | |
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 1 | 12 | |
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 | ||
or COMPSCI 723 | Natural Language Processing | |
Introduction to Text Retrieval and Its Applications in Biomedicine | ||
or COMPSCI 744 | 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 | ||
Cartography | ||
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 Credits | 30 |
- 1
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.
- 2
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).
- 3
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.
Data Science MS Learning Outcomes
Students in the M.S. program in Data Science 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.
- Recognize and internalize the importance of ethical use of data and data science.