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.
Admission
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.
Code | Title | Credits |
---|---|---|
Preparatory Courses | ||
Mathematics | ||
One of the following calculus sequences (or an equivalent) ^{1} | 8-12 | |
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 234 | Linear Algebra and Differential Equations | 3-4 |
or MATH 240 | Matrices and Applications | |
Computer Science | ||
COMPSCI 250 | Introductory Computer Programming | 3 |
COMPSCI 251 | Intermediate Computer Programming | 3 |
Statistics | ||
MTHSTAT 215 | Elementary Statistical Analysis | 3 |
or IND ENG 367 | Introductory Statistics for Physical Sciences and Engineering Students | |
MTHSTAT 216 | Introduction to Statistical Computing and Data Science | 3 |
Total Credits | 23-28 |
- ^{ 1 }
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).
Code | Title | Credits |
---|---|---|
Core Courses | ||
Statistics | ||
MTHSTAT 361 | Introduction to Mathematical Statistics I | 3 |
MTHSTAT 362 | Introduction to Mathematical Statistics II | 3 |
MTHSTAT 563 | Regression Analysis | 3 |
MTHSTAT 566 | Computational Statistics | 3 |
MTHSTAT 568 | Multivariate Statistical Analysis | 3 |
Computer Science | ||
COMPSCI 317 | Discrete Information Structures | 3 |
or MATH 341 | Seminar: Introduction to the Language and Practice of Mathematics | |
COMPSCI 351 | Data Structures and Algorithms | 3 |
COMPSCI 395 | Social, Professional, and Ethical Issues | 3 |
or PHILOS 237 | Technology, Values, and Society | |
COMPSCI 422 | Introduction to Artificial Intelligence | 3 |
COMPSCI 411 | Machine Learning and Applications | 3 |
or COMPSCI 425 | Introduction to Data Mining | |
COMPSCI 557 | Introduction to Database Systems | 3 |
Communication and Ethics | ||
ENGLISH 310 | Writing, Speaking, and Technoscience in the 21st Century | 3 |
Total Credits | 36 |
Code | Title | Credits |
---|---|---|
Capstone Experience (select one of the options below) | ||
MTHSTAT 489 | Internship in Mathematical Statistics, Upper Division | 1-6 |
MATH 599 | Capstone Experience | 1 |
COMPSCI 595 | Capstone Project | 3 |
COMPSCI 599 | Senior Thesis | 3 |
Code | Title | Credits |
---|---|---|
Electives (to reach 120 total credits) | ||
Suggested are courses with substantial data analysis, data processing, or computational content, such as: | ||
COMPSCI 315 | Introduction to Computer Organization and Assembly Language Programming | 3 |
COMPSCI 411 | Machine Learning and Applications | 3 |
COMPSCI 423 | Introduction to Natural Language Processing | 3 |
COMPSCI 425 | Introduction to Data Mining | 3 |
COMPSCI 444 | Introduction to Text Retrieval and Its Applications in Biomedicine | 3 |
COMPSCI 459 | Fundamentals of Computer Graphics | 3 |
COMPSCI 469 | Introduction to Computer Security | 3 |
COMPSCI 535 | Algorithm Design and Analysis | 3 |
MTHSTAT 562 | Design of Experiments | 3 |
MTHSTAT 564 | Time Series Analysis | 3 |
MTHSTAT 565 | Nonparametric Statistics | 3 |
MATH 315 | Mathematical Programming and Optimization | 3 |
MATH 318 | Topics in Discrete Mathematics | 3 |
MATH 583 | Introduction to Probability Models | 3 |
INFOST 120 | Information Technology Ethics | 3 |
INFOST 315 | Knowledge Organization for Information Science and Technology | 3 |
INFOST 465 | Legal Aspects of Information Products and Services | 3 |
INFOST 660 | Information Policy | 3 |
INFOST 661 | Information Ethics | 3 |