The mathematics named option programs allow students to develop a deep understanding of how the subject relates to other areas of human inquiry. The requirements for these programs 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 program 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 10 distinct courses for at least 30 credits as described below. Note that while some courses may be used to fulfill more than one requirement it is still considered only a single course and may only contribute once to the total course count. Finally, at most one course from each of the following groupings may be used to fulfill the minimum course and credit requirement (i.e.: minimum of ten courses and at least 30 credits): Intro Linear Algebra (MATH 320, MATH 340, MATH 341, MATH 375), Intro Differential Equations (MATH 319, MATH 320 or MATH 376), and and Intro Probability (MATH/STAT 309 or MATH/STAT 431).
Code | Title | Credits |
---|---|---|
Core Math Requirement (minimum of six distinct MATH courses for at least 18 credits) | ||
Linear Algebra | 3-5 | |
Linear Algebra | ||
or MATH 320 | Linear Algebra and Differential Equations | |
or MATH 340 | Elementary Matrix and Linear Algebra | |
or MATH 375 | Topics in Multi-Variable Calculus and Linear Algebra | |
Intermediate Mathematics Requirement (complete at least one) | 0-6 | |
The Theory of Single Variable Calculus | ||
Linear Algebra | ||
Applied Mathematical Analysis and Applied Mathematical Analysis | ||
Topics in Multi-Variable Calculus and Linear Algebra | ||
Probability (complete at least one) | 3 | |
Introduction to the Theory of Probability | ||
Introduction to Probability and Mathematical Statistics I | ||
Probability Theory | ||
Numerical and optimization methods (complete at least one) | 3 | |
Numerical Linear Algebra | ||
Linear Optimization | ||
Numerical Analysis | ||
Applied Linear Algebra | ||
Introduction to Combinatorial Optimization | ||
Mathematics of data | 3 | |
Mathematical Methods in Data Science | ||
Advanced Electives (complete at least one): | 0-3 | |
Numerical Linear Algebra | ||
Numerical Analysis | ||
Analysis I | ||
Linear Optimization | ||
Probability Theory | ||
Linear Algebra II | ||
Data-Driven Dynamical Systems, Stochastic Modeling and Prediction | ||
Introduction to Stochastic Processes | ||
Electives to reach required six courses for at least 18 credits in MATH ^{1} | 0-6 | |
Introduction to Probability and Mathematical Statistics II | ||
Introduction to Combinatorial Optimization | ||
Applied Linear Algebra | ||
Graphs and Networks in Data Science | ||
Numerical Linear Algebra | ||
Numerical Analysis | ||
Analysis I | ||
Linear Optimization | ||
Probability Theory | ||
Linear Algebra II | ||
Data-Driven Dynamical Systems, Stochastic Modeling and Prediction | ||
Introduction to Stochastic Processes | ||
Data Science Requirement (at least four courses for at least 12 credits) ^{2} | 12 | |
Data Science Fundamentals (choose one) | ||
Data Science Modeling II | ||
Data Science Programming II | ||
Remaining courses may be selected from below or from the MATH elective lists above. ^{3} | ||
Introduction to Optimization | ||
Image Processing | ||
Introduction to Artificial Neural Networks | ||
Introduction to Artificial Intelligence | ||
Probability and Information Theory in Machine Learning | ||
Medical Image Analysis | ||
Introduction to Bioinformatics | ||
Introductory Nonparametric Statistics | ||
Applied Categorical Data Analysis | ||
Statistical Experimental Design | ||
Data Science with R | ||
Classification and Regression Trees | ||
Introduction to Deep Learning and Generative Models | ||
Applied Multivariate Analysis | ||
Financial Statistics | ||
Introduction to Computational Statistics | ||
Statistical Methods for Clinical Trials | ||
Statistical Methods for Epidemiology | ||
Introduction to Applied Econometrics | ||
Introductory Econometrics | ||
Fundamentals of Data Analytics for Economists | ||
Fundamentals of Industrial Data Analytics | ||
Information Sensing and Analysis for Manufacturing Processes | ||
Data Driven Engineering Design | ||
Total Credits | 30 |
Residence and Quality of Work
- 2.000 GPA on all MATH courses and courses eligible for the major.^{4}
- 2.000 GPA on at least 15 credits of upper level credit in the major.^{5}
- 15 credits in MATH in the major taken on the UW-Madison campus.^{6}
Footnotes
- ^{ 1 }
Elective courses must be distinct from those used to fulfill the above requirements.
- ^{ 2 }
Courses below may have prerequisites outside of this program.
- ^{ 3 }
MATH courses must be distinct from any used to fulfill an above requirement.
- ^{ 4 }
This includes any course with a MATH prefix (or crosslisted with MATH) regardless of its appearance in the tables above and any non-MATH class explicitly listed in the tables above.
- ^{ 5 }
This includes any MATH course (including those crosslisted with MATH) numbered 307 and above, regardless of its appearance in the tables above, as well as only those non-MATH classes which appear in the tables above and have the advanced LAS attribute.
- ^{ 6 }
This includes any MATH course (and those crosslisted with MATH) numbered 307 and above.
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 Intermediate level course
- Additional intermediate level courses as needed
- Required advanced level course
- Additional advanced level courses
Freshman | |||
---|---|---|---|
Fall | Credits | Spring | Credits |
MATH 221 | 5 | MATH 222 | 4 |
Literature Breadth | 3 | Literature Breadth | 3 |
Communication A | 3 | Ethnic Studies | 3 |
Foreign Language | 4 | Foreign Language | 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 | 3 | Physical Science Breadth | 3 |
Elective | 3 | Elective | 3 |
16 | 15 | ||
Junior | |||
Fall | Credits | Spring | Credits |
Required Intermediate MATH | 3 | MATH Elective | 3 |
Data Science Fundamentals Course | 3 | Data Science Elective | 3 |
Social Sciences Breadth | 3 | Social Science Breadth | 3 |
Biological Sciences Breadth | 3 | Biological Sciences Breadth | 3 |
Elective | 3 | Elective | 3 |
15 | 15 | ||
Senior | |||
Fall | Credits | Spring | Credits |
MATH 535 | 3 | Advanced 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 |