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).

Core Math Requirement (minimum of six distinct MATH courses for at least 18 credits)
Linear Algebra3-5
Linear Algebra
Linear Algebra and Differential Equations
Elementary Matrix and Linear Algebra
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 data3
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 10-6
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) 212
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 Credits30

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:

  1. Calculus
  2. Linear Algebra
  3. Required Intermediate level course
  4. Additional intermediate level courses as needed
  5. Required advanced level course
  6. Additional advanced level courses
Freshman
FallCreditsSpringCredits
MATH 2215MATH 2224
Literature Breadth 3Literature Breadth 3
Communication A 3Ethnic Studies 3
Foreign Language4Foreign Language4
 15 14
Sophomore
FallCreditsSpringCredits
MATH 2344MATH Required Linear Algebra3
Humanities Breadth 3MATH Required Probability3
Communication B 3Humanities Breadth 3
Prerequisite for Data Science Fundamentals course3Physical Science Breadth 3
Elective3Elective3
 16 15
Junior
FallCreditsSpringCredits
Required Intermediate MATH3MATH Elective3
Data Science Fundamentals Course3Data Science Elective3
Social Sciences Breadth 3Social Science Breadth 3
Biological Sciences Breadth 3Biological Sciences Breadth 3
Elective3Elective3
 15 15
Senior
FallCreditsSpringCredits
MATH 5353Advanced MATH elective3
Data Science Elective3Data Science Elective3
Social Science Breadth 3Social Science Breadth 3
Electives 6Electives6
 15 15
Total Credits 120