Degree Requirements

To clarify any degree requirements, please contact the Data Science Program Coordinator.

Data Science Program Requirements

Students who intend to complete the BS in Data Science (BSDS) program in four years will need to begin taking mathematics in their first semester. Such students should have a University of Wisconsin-Milwaukee mathematics placement level of 30 (ready for precalculus) or better.


For admission to the BSDS program, students need only meet the general requirements of admission to UW-Milwaukee. 

As soon as students realize their interest in the BSDS degree, they should consult with an BSDS advisor either in the College of Engineering and Applied Science or College of Letters and Science, who will assist in planning a program.

Degree Requirements

The program requires at least 120 credits, which include University-wide General Education Requirements, 23-28 credits of mandatory preparatory courses, 36 credits of mandatory advanced core courses, a capstone course or an internship at the end of the coursework, and additional elective courses to fulfill the overall credit requirement.

An average GPA of 2.000 on all coursework attempted at UWM is required for this degree. In addition, students must achieve an average 2.000 GPA on all coursework attempted, including transfer work. A minimum 2.000 GPA must be earned, on average, on 300-level and above courses taken to satisfy the advanced requirements. Students satisfy the residency requirement for the degree by completing at UWM both a minimum of 15 credits of the required advanced courses and one of the following:

  • The last 30 credits;
  • 45 of the last 60 credits;
  • Any 90 credits.
Preparatory Courses
One of the following calculus sequences (or an equivalent) 18-12
Calculus and Analytic Geometry I
and Calculus and Analytic Geometry II
and Calculus and Analytic Geometry III
Survey in Calculus and Analytic Geometry I
and Survey in Calculus and Analytic Geometry II
MATH 234Linear Algebra and Differential Equations3-4
or MATH 240 Matrices and Applications
Computer Science
COMPSCI 250Introductory Computer Programming3
COMPSCI 251Intermediate Computer Programming3
MTHSTAT 215Elementary Statistical Analysis3
or IND ENG 367 Introductory Statistics for Physical Sciences and Engineering Students
MTHSTAT 216Introduction to Statistical Computing and Data Science3
Total Credits23-28

One equivalent sequence accepted is MATH 221 & MATH 222, or a student may replace MATH 211 or MATH 231 with MATH 213 (for other combinations see advisor).

Core Courses
MTHSTAT 361Introduction to Mathematical Statistics I3
MTHSTAT 362Introduction to Mathematical Statistics II3
MTHSTAT 563Regression Analysis3
MTHSTAT 566Computational Statistics3
MTHSTAT 568Multivariate Statistical Analysis3
Computer Science
COMPSCI 317Discrete Information Structures3
or MATH 341 Seminar: Introduction to the Language and Practice of Mathematics
COMPSCI 351Data Structures and Algorithms3
COMPSCI 395Social, Professional, and Ethical Issues3
or PHILOS 237 Technology, Values, and Society
COMPSCI 422Introduction to Artificial Intelligence3
COMPSCI 411Machine Learning and Applications3
or COMPSCI 425 Introduction to Data Mining
COMPSCI 557Introduction to Database Systems3
Communication and Ethics
ENGLISH 310Writing, Speaking, and Technoscience in the 21st Century3
Total Credits36
Capstone Experience (select one of the options below)
MTHSTAT 489Internship in Mathematical Statistics, Upper Division1-6
MATH 599Capstone Experience1
COMPSCI 595Capstone Project3
COMPSCI 599Senior Thesis3
Electives (to reach 120 total credits)
Suggested are courses with substantial data analysis, data processing, or computational content, such as:
COMPSCI 315Introduction to Computer Organization and Assembly Language Programming3
COMPSCI 411Machine Learning and Applications3
COMPSCI 423Introduction to Natural Language Processing3
COMPSCI 425Introduction to Data Mining3
COMPSCI 444Introduction to Text Retrieval and Its Applications in Biomedicine3
COMPSCI 459Fundamentals of Computer Graphics3
COMPSCI 469Introduction to Computer Security3
COMPSCI 535Algorithm Design and Analysis3
MTHSTAT 562Design of Experiments3
MTHSTAT 564Time Series Analysis3
MTHSTAT 565Nonparametric Statistics3
MATH 315Mathematical Programming and Optimization3
MATH 318Topics in Discrete Mathematics3
MATH 583Introduction to Probability Models3
INFOST 120Information Technology Ethics3
INFOST 315Knowledge Organization for Information Science and Technology3
INFOST 465Legal Aspects of Information Products and Services3
INFOST 660Information Policy3
INFOST 661Information Ethics3