The Mathematics major’s named options allow students to develop a deep understanding of how the subject relates to other areas of human inquiry. The requirements for these options feature mathematics courses with topics inspired by and commonly applied to problems in these associated fields. Though often paired with a second major in a related area, these programs function well alone and are suited to any mathematics student with a variety of interests. Students interested in a named option are recommended to meet with an advisor to navigate the various plans and courses available to them. Advising information can be found on the BA or BS pages.
The named options do not support Honors in the Major.
Requirements
The Mathematics for Data Science program requires at least 10 courses for at least 30 credits as described below.
Core Math Requirement
Complete at least six MATH courses for at least 18 credits.
Linear Algebra
Complete one course from the list below. Only one of these courses will be used to fulfill minimum course/credit requirements for the major.
| Code | Title | Credits |
|---|---|---|
| MATH 341 | Linear Algebra | 3-5 |
| or MATH 320 | Linear Algebra and Differential Equations | |
| or MATH 340 | Elementary Matrix and Linear Algebra | |
| or MATH 345 | Linear Algebra and Optimization | |
| or MATH 375 | Topics in Multi-Variable Calculus and Linear Algebra | |
Transition to Advanced Mathematics
Complete one course or sequence from the list below. If a student takes MATH 341 or MATH 375 to complete the Linear Algebra requirement, they may also use that course for this requirement. The course and credits will only count once toward the course/credit requirements for the major.
| Code | Title | Credits |
|---|---|---|
| MATH 341 | Linear Algebra | 3-5 |
| or MATH 375 | Topics in Multi-Variable Calculus and Linear Algebra | |
| MATH 421 | The Theory of Single Variable Calculus | 3 |
| MATH 321 & MATH 322 | Applied Mathematical Analysis 1: Vector and Complex Calculus and Applied Mathematical Analysis 2: Partial Differential Equations | 6 |
Probability
Complete one course from:
| Code | Title | Credits |
|---|---|---|
| MATH/STAT 431 | Introduction to the Theory of Probability | 3 |
| or MATH/STAT 309 | Introduction to Probability and Mathematical Statistics I | |
At most one course in Introductory Probability (MATH/STAT 309 and MATH/STAT 431) may be used to fulfill the course/credit requirements for the major. | ||
| MATH 531 | Probability Theory | 3 |
Numerical and Optimization Methods
Complete one course from:
| Code | Title | Credits |
|---|---|---|
| MATH/COMP SCI 513 | Numerical Linear Algebra | 3 |
| MATH/COMP SCI/I SY E/STAT 525 | Linear Optimization | 3 |
| MATH/COMP SCI 514 | Numerical Analysis | 3 |
| MATH 443 | Applied Linear Algebra | 3 |
| MATH/COMP SCI/I SY E 425 | Introduction to Combinatorial Optimization | 3 |
Mathematics of Data
Complete one course from:
| Code | Title | Credits |
|---|---|---|
| MATH 535 | Mathematical Methods in Data Science | 3 |
MATH Electives
Complete at least six MATH courses for at least 18 credits to satisfy the overall requirements of the major by choosing additional MATH courses from the lists below.
At least one MATH elective must be chosen from the list of Advanced MATH Elective courses. The remaining courses required to reach the required minimum courses and credits may be chosen from either the list of Advanced MATH Elective courses or the Additional MATH Electives.
Advanced MATH Elective
If a student takes MATH 531 to complete the Probability requirement, they may also use that course for this requirement. If a student takes MATH/COMP SCI 513, MATH/COMP SCI/I SY E/STAT 525, or MATH/COMP SCI 514 to complete the Numerical and Optimization Methods requirement, they may also use that course for this requirement. In either case, the course and credits will count only once toward the course/credit requirements for the major.
| Code | Title | Credits |
|---|---|---|
| MATH/COMP SCI 513 | Numerical Linear Algebra | 3 |
| MATH/COMP SCI 514 | Numerical Analysis | 3 |
| MATH 521 | Analysis I | 3 |
| MATH/COMP SCI/I SY E/STAT 525 | Linear Optimization | 3 |
| MATH 531 | Probability Theory | 3 |
| MATH 540 | Linear Algebra II | 3 |
| MATH 616 | Data-Driven Dynamical Systems, Stochastic Modeling and Prediction | 3 |
| MATH/I SY E/OTM/STAT 632 | Introduction to Stochastic Processes | 3 |
Additional MATH Electives
| Code | Title | Credits |
|---|---|---|
| MATH/STAT 310 | Introduction to Probability and Mathematical Statistics II | 3 |
| MATH 321 | Applied Mathematical Analysis 1: Vector and Complex Calculus | 3 |
| MATH 322 | Applied Mathematical Analysis 2: Partial Differential Equations | 3 |
| MATH 376 | Topics in Multi-Variable Calculus and Differential Equations | 5 |
| MATH 421 | The Theory of Single Variable Calculus | 3 |
| MATH/COMP SCI/I SY E 425 | Introduction to Combinatorial Optimization | 3 |
| MATH 443 | Applied Linear Algebra | 3 |
| MATH 444 | Graphs and Networks in Data Science | 3 |
Data Science Requirement
Complete at least four courses for at least 12 credits. Each course that satisfies this requirement must be distinct from those satisfying any part of the Core Math requirement. Courses below may have prerequisites outside of the requirements for this named option.
Data Science Fundamentals
Complete one course from:
| Code | Title | Credits |
|---|---|---|
| STAT 340 | Data Science Modeling II | 4 |
| COMP SCI 320 | Data Science Programming II | 4 |
Data Science Electives
To reach the 4 courses for at least 12 credits required, students may complete an additional Data Science Fundamentals course, additional courses from the MATH electives lists above, or any of the following courses.
Approved Elective Courses
| Code | Title | Credits |
|---|---|---|
| COMP SCI/E C E/I SY E 524 | Introduction to Optimization | 3 |
| COMP SCI/E C E 533 | Image Processing | 3 |
| COMP SCI/E C E/M E 539 | Introduction to Artificial Neural Networks | 3 |
| COMP SCI 540 | Introduction to Artificial Intelligence | 3 |
| COMP SCI 541 | Theory & Algorithms for Data Science | 3 |
| COMP SCI/E C E 561 | Probability and Information Theory in Machine Learning | 3 |
| COMP SCI/B M I 567 | Biomedical Image Analysis | 3 |
| COMP SCI/B M I 576 | Introduction to Bioinformatics | 3 |
| STAT 351 | Introductory Nonparametric Statistics | 3 |
| STAT 421 | Applied Categorical Data Analysis | 3 |
| STAT 424 | Statistical Experimental Design | 3 |
| STAT 433 | Data Science with R | 3 |
| STAT 443 | Classification and Regression Trees | 3 |
| STAT 453 | Introduction to Deep Learning and Generative Models | 3 |
| STAT 456 | Applied Multivariate Analysis | 3 |
| STAT 461 | Financial Statistics | 3 |
| STAT/COMP SCI 471 | Introduction to Computational Statistics | 3 |
| STAT/B M I 641 | Statistical Methods for Clinical Trials | 3 |
| STAT/B M I 642 | Statistical Methods for Epidemiology | 3 |
| ECON 400 | Introduction to Applied Econometrics | 4 |
| ECON 410 | Introductory Econometrics | 4 |
| ECON 570 | Fundamentals of Data Analytics for Economists | 3-4 |
| I SY E 412 | Fundamentals of Industrial Data Analytics | 3 |
| I SY E 612 | Information Sensing and Analysis for Manufacturing Processes | 3 |
| M E 536 | Machine Learning for Data-Driven Engineering Design | 3 |
Residence and Quality of Work
- 2.000 GPA on all MATH courses and courses eligible for the major.
- This includes all MATH courses (including those cross-listed with MATH), regardless of appearing in the requirements of the program, and any non-MATH course that meets a requirement in the program.
- 2.000 GPA on at least 15 credits of upper level credit in the major.
- This includes all MATH courses numbered 307 and above (including those cross-listed with MATH), regardless of appearing in the requirements of the program, and any non-MATH courses that meet a requirement in the program and carry the Advanced level designation.
- 15 credits in MATH in the major taken on the UW-Madison campus.
- This includes all MATH courses numbered 307 and above (including those cross-listed with MATH), regardless of appearing in the requirements of the program.
Four-Year Plan
This Four-Year Plan is only one way a student may complete an L&S degree with this major. Many factors can affect student degree planning, including placement scores, credit for transferred courses, credits earned by examination, and individual scholarly interests. In addition, many students have commitments (e.g., athletics, honors, research, student organizations, study abroad, work and volunteer experiences) that necessitate they adjust their plans accordingly. Informed students engage in their own unique Wisconsin Experience by consulting their academic advisors, Guide, DARS, and Course Search & Enroll for assistance making and adjusting their plan.
In general, your four year plan in mathematics should be organized along the following sequence:
- Calculus
- Linear Algebra
- Required Transition to Advanced Math course
- Additional 300/400-level courses as needed
- Required Advanced MATH course
- Additional 500/600-level MATH courses
| Freshman | |||
|---|---|---|---|
| Fall | Credits | Spring | Credits |
| MATH 221 | 5 | MATH 222 | 4 |
| Literature Breadth | 3 | Literature Breadth | 3 |
| Communication A | 3 | Ethnic Studies | 3 |
| Language (if required) | 4 | Language (if required) | 4 |
| 15 | 14 | ||
| Sophomore | |||
| Fall | Credits | Spring | Credits |
| MATH 234 | 4 | MATH Required Linear Algebra | 3 |
| Humanities Breadth | 3 | MATH Required Probability | 3 |
| Communication B | 3 | Humanities Breadth | 3 |
| Prerequisite for Data Science Fundamentals course | 4 | Physical Science Breadth | 3 |
| INTER-LS 210 | 1 | Elective | 3 |
| 15 | 15 | ||
| Junior | |||
| Fall | Credits | Spring | Credits |
| Required Transition to Advanced Math | 3 | 300/400-level MATH Elective | 3 |
| Data Science Fundamentals Course | 4 | Data Science Elective | 3 |
| Social Sciences Breadth | 3 | Social Science Breadth | 3 |
| Biological Sciences Breadth | 3 | Biological Sciences Breadth | 3 |
| Elective | 3 | Elective | 3 |
| 16 | 15 | ||
| Senior | |||
| Fall | Credits | Spring | Credits |
| MATH 535 | 3 | 500/600-level MATH elective | 3 |
| Data Science Elective | 3 | Data Science Elective | 3 |
| Social Science Breadth | 3 | Social Science Breadth | 3 |
| Electives | 6 | Electives | 6 |
| 15 | 15 | ||
| Total Credits 120 | |||