Students in the Data Science major will be able to apply computational, mathematical, and statistical thinking to data-rich problems in a wide variety of fields in a responsible and ethical manner. This includes the ability to manage, process, model, gain meaning and knowledge, and present data. Data Science is one of the fastest growing career sectors in Wisconsin and across the nation.

By its very nature, the field of data science is one that teaches novel and cutting-edge ways to engage in the “continual sifting and winnowing by which alone the truth can be found.”

To declare the data science major, student should meet with a data science major advisor prior to attaining senior standing (86 credits).

Students must have a 2.000 GPA on coursework counting in the major, and a 2.000 GPA on any upper-level work in the major completed prior to declaration. No specific coursework must be completed to declare.

Please see the Data Science major page on the Department of Statistics website for information on how to declare the major and meet with advisors.

Students declared in the Data Science certificate may not be declared in the Data Science major at the same time. Students who do wish to declare this major must first cancel their declaration in the Data Science certificate.

Students declared in the Statistics certificate may not be declared in the Data Science major at the same time. Students who do wish to declare this major must first cancel their declaration in the Statistics certificate.

University General Education Requirements

All undergraduate students at the University of Wisconsin–Madison are required to fulfill a minimum set of common university general education requirements to ensure that every graduate acquires the essential core of an undergraduate education. This core establishes a foundation for living a productive life, being a citizen of the world, appreciating aesthetic values, and engaging in lifelong learning in a continually changing world. Various schools and colleges will have requirements in addition to the requirements listed below. Consult your advisor for assistance, as needed. For additional information, see the university Undergraduate General Education Requirements section of the Guide.

General Education
  • Breadth—Humanities/Literature/Arts: 6 credits
  • Breadth—Natural Science: 4 to 6 credits, consisting of one 4- or 5-credit course with a laboratory component; or two courses providing a total of 6 credits
  • Breadth—Social Studies: 3 credits
  • Communication Part A & Part B *
  • Ethnic Studies *
  • Quantitative Reasoning Part A & Part B *

* The mortarboard symbol appears before the title of any course that fulfills one of the Communication Part A or Part B, Ethnic Studies, or Quantitative Reasoning Part A or Part B requirements.

College of Letters & Science Degree Requirements: Bachelor of Science (B.S.)

Students pursuing a Bachelor of Science degree in the College of Letters & Science must complete all of the requirements below. The College of Letters & Science allows this major to be paired with either the Bachelor of Arts or the Bachelor of Science degree requirements.


Mathematics Complete two courses of 3+ credits at the Intermediate or Advanced level in MATH, COMP SCI, or STAT subjects. A maximum of one course in each of COMP SCI and STAT subjects counts toward this requirement.
Foreign Language Complete the third unit of a foreign language.
L&S Breadth Complete:
• 12 credits of Humanities, which must include at least 6 credits of Literature; and
• 12 credits of Social Science; and
• 12 credits of Natural Science, which must include 6 credits of Biological Science and 6 credits of Physical Science.
Liberal Arts and Science Coursework Complete at least 108 credits.
Depth of Intermediate/Advanced Coursework Complete at least 60 credits at the Intermediate or Advanced level.
Major Declare and complete at least one major.
Total Credits Complete at least 120 credits.
UW-Madison Experience Complete both:
• 30 credits in residence, overall, and
• 30 credits in residence after the 86th credit.
Quality of Work • 2.000 in all coursework at UW–Madison
• 2.000 in Intermediate/Advanced level coursework at UW–Madison


Non–L&S students who have permission from their school/college to pursue an additional major within L&S only need to fulfill the major requirements. They do not need to complete the L&S Degree Requirements above.

Requirements for the Major

Foundational Math Courses
MATH 221 Calculus and Analytic Geometry 15
or MATH 217 Calculus with Algebra and Trigonometry II
or MATH 275
MATH 222 Calculus and Analytic Geometry 24
or MATH 276
Total Credits9
Foundational Data Science Courses
STAT 240 Data Science Modeling I4
STAT 340 Data Science Modeling II4
COMP SCI 220 Data Science Programming I4
or COMP SCI 300 Programming II
COMP SCI 320 Data Science Programming II4
L I S 461 Data and Algorithms: Ethics and Policy3-4
Total Credits19-20
Students must complete at least one course from each of the four following categories, plus additional electives to reach the minimum credits. Additional courses taken within each category (except for linear algebra) may count towards other electives. 2
Machine Learning3
Complete one of the following:
Matrix Methods in Machine Learning
Introduction to Artificial Neural Networks
Introduction to Artificial Intelligence
Machine Learning for Business Analytics
Machine Learning in Action for Industrial Engineers
Mathematical Methods in Data Science
Introduction to Machine Learning and Statistical Pattern Classification
Introduction to Deep Learning and Generative Models
Advanced Computing3
Complete one of the following:
Programming III
Introduction to Numerical Methods
Introduction to Computational Statistics
Numerical Linear Algebra
Numerical Analysis
Introduction to Optimization
Introduction to Big Data Systems
Database Management Systems: Design and Implementation
Introduction to Bioinformatics
Advanced Geocomputing and Geospatial Big Data Analytics
Geospatial Database Design and Development
Statistical Modeling3
Complete one of the following:
Introduction to Applied Econometrics
Introductory Econometrics
Economic Forecasting
GIS and Spatial Analysis
Introduction to Quality Engineering
Introduction to Probability and Mathematical Statistics I 2
Introduction to Theory and Methods of Mathematical Statistics I
Introduction to the Theory of Probability
Introduction to Probability and Mathematical Statistics II 2
Introduction to Theory and Methods of Mathematical Statistics II
Introduction to Time Series
Introductory Nonparametric Statistics
Applied Categorical Data Analysis
Statistical Experimental Design
Statistical Data Visualization
Classification and Regression Trees
Applied Multivariate Analysis
Financial Statistics
Probability Theory
Introduction to Stochastic Processes
An Introduction to Brownian Motion and Stochastic Calculus
Linear Algebra3
Complete one from the following. Only one course from the linear algebra list can be used towards the major: 2
Linear Algebra and Differential Equations
Elementary Matrix and Linear Algebra
Linear Algebra
Topics in Multi-Variable Calculus and Linear Algebra
Other Electives6
For additional electives students may complete courses from the list below or additional courses from the required categories above: 2
Introduction to Combinatorial Optimization
Linear Optimization
Image Processing
Computer Graphics
Medical Image Analysis
Introduction to Algorithms
Signals, Information, and Computation
Data Visualization for Economists
Fundamentals of Data Analytics for Economists
Topics in Economic Data Analysis
Introduction to Geocomputing
Graphic Design in Cartography
Interactive Cartography & Geovisualization
Operations Research-Deterministic Modeling
Fundamentals of Industrial Data Analytics
Inspection, Quality Control and Reliability
Information Sensing and Analysis for Manufacturing Processes
Introduction to Databases
Data Storytelling with Visualization
Applied Database Design
Introduction to Text Mining
Introduction to Survey Methods for Social Research
Practicum in Analysis and Research
Data Science Computing Project
Data Science with R
Total Credits18

Residence & Quality of Work

  • 2.000 GPA in all major courses
  • 2.000 GPA in all upper level work in the major1
  • 15 credits in the major, taken on the UW-Madison campus



Upper-level in the major includes L I S 461 and all courses listed in the Data Science Electives (i.e. Machine Learning, Advanced Computing, Statistical Modeling, Linear Algebra, and Other Electives).


Students are only allowed to count one course from each of probability (STAT/​MATH  309, STAT 311, or STAT/​MATH  431), inference (STAT/​MATH  310 or STAT 312), and linear algebra (MATH 320, MATH 340, MATH 341, or MATH 375) towards the major.

University Degree Requirements

Total Degree To receive a bachelor's degree from UW–Madison, students must earn a minimum of 120 degree credits. The requirements for some programs may exceed 120 degree credits. Students should consult with their college or department advisor for information on specific credit requirements.
Residency Degree candidates are required to earn a minimum of 30 credits in residence at UW–Madison. "In residence" means on the UW–Madison campus with an undergraduate degree classification. “In residence” credit also includes UW–Madison courses offered in distance or online formats and credits earned in UW–Madison Study Abroad/Study Away programs.
Quality of Work Undergraduate students must maintain the minimum grade point average specified by the school, college, or academic program to remain in good academic standing. Students whose academic performance drops below these minimum thresholds will be placed on academic probation.
  1. Integrate foundational concepts and tools from mathematics, computer science, and statistics to solve data science problems.
  2. Demonstrate competencies with tools and processes necessary for data management and reproducibility.
  3. Produce meaning from data employing modeling strategies.
  4. Demonstrate critical thinking related to data science concepts and methods.
  5. Conduct data science activities aware of and according to policy, privacy, security and ethical considerations.
  6. Demonstrate oral, written, and visual communication skills related to data science.

Sample Four-Year Plan

This Sample Four-Year Plan is a tool to assist students and their advisor(s). Students should use it—along with their DARS report, the Degree Planner, and Course Search & Enroll tools—to make their own four-year plan based on their placement scores, credit for transferred courses and approved examinations, and individual interests. As students become involved in athletics, honors, research, student organizations, study abroad, volunteer experiences, and/or work, they might adjust the order of their courses to accommodate these experiences. Students will likely revise their own four-year plan several times during college.

Communication A3MATH 2215
Biological Science Breadth3Ethnic Studies3
Foreign Language (if needed)4Foreign Language (if needed)4
 14 16
MATH 2224STAT 3404
STAT 2404Linear Algebra course3
Literature Breadth3Humanities Breadth3
Physical Science Breadth3Literature Breadth3
INTER-LS 2101Social Science Breadth3
 15 16
L I S 461 (enroll in Communication B section)3-4Statistical Modeling course3
Machine Learning course3Physical Science Breadth3
Biological Science Breadth3Social Science Breadth3
Social Science Breadth3Electives6
 16 15
Advanced Computing course3Data Science elective3
Data Science elective3Electives10
Social Science Breadth3 
 15 13
Total Credits 120

Sample Three-Year Plan

This Sample Three-Year Plan is a tool to assist students and their advisor(s). Students should use it —along with their DARS report, the Degree Planner, and Course Search & Enroll tools — to make their own three-year plan based on their placement scores, credit for transferred courses and approved examinations, and individual interests.

Three-year plans may vary considerably from student to student, depending on their individual preparation and circumstances. Students interested in graduating in three years should meet with an advisor as early as possible to discuss feasibility, appropriate course sequencing, post-graduation plans (careers, graduate school, etc.), and opportunities they might forgo in pursuit of a three-year graduation plan.

Departmental Expectations

A three-year degree is feasible for students with a variety of backgrounds and specific preparation. Students should ideally be entering the University with a minimum of 30 advanced standing credits, and have satisfied the following requirements with course credit or via placement examination:

  • MATH 221 Calculus and Analytic Geometry 1
  • MATH 222 Calculus and Analytic Geometry 2
  • 3-4 units of foreign language
First Year
STAT 2404STAT 3404
Communications A complete during first year3Ethnics Studies complete within first 60 credits3
Social Science Breadth3Humanities Breadth3
 14 14
Second Year
L I S 461 (meets Humanities Breadth, 4cr section meets Communication B)3-4Machine Learning Course3
Linear Algebra Course3Statistical Modeling Course3
Biological Science Breadth3Literature Breadth3
Social Science Breadth3Physical Science Breadth3
Elective3-4INTER-LS 2101
 16 16
Third Year
Advanced Computing Course3Data Science Elective3
Data Science Elective3Literature Breath3
Science Breadth3Science Breadth3
Social Science Breadth6Electives6
 15 15
Total Credits 90

Looking for Data Science advising?

Information on group declaration sessions, individual advising appointments, drop-in advising, and contact information for advisors is available on our website.

What do Data Scientists Do?

Data Scientists are trained to manage, process, model, gain meaning and knowledge, and present data.  These skills can be employed in a wide variety of different sectors of employment.  Examples of interests of our students include finance, banking, sports analytics, marketing, retail, humanities, psychology, biosciences, healthcare, and consulting, just to name a few.  Students are encouraged to combine data science with majors, certificates, and courses from differing areas to best be able to apply their data science in the area of their choosing.

Data science is one of the fastest growing area of jobs in the U.S. and in Wisconsin. All of the major job search engines regularly list a multitude of positions, for example, in 2022 Data Scientist was the #3 job on the web site Glassdoor with over 10,000 jobs, Indeed.com had over 20,000 jobs for data science, and thousands of positions in multiple data oriented categories can be found on Monster.com.

Additionally, the Occupational Outlook Handbook (OOH) from the Bureau of Labor Statistics shows the job growth outlook from 2021-31 for Data Scientists to be 36% (much faster than average).

Some students may want to continue to develop additional advanced data science skills through graduate education.

L&S career resources

Every L&S major opens a world of possibilities.  SuccessWorks at the College of Letters & Science helps students turn the academic skills learned in their major, certificates, and other coursework into fulfilling lives after graduation, whether that means jobs, public service, graduate school or other career pursuits.

In addition to providing basic support like resume reviews and interview practice, SuccessWorks offers ways to explore interests and build career skills from their very first semester/term at UW all the way through graduation and beyond.

Students can explore careers in one-on-one advising, try out different career paths, complete internships, prepare for the job search and/or graduate school applications, and connect with supportive alumni and even employers in the fields that inspire them.

Advising Staff

Information regarding the Data Science advisors and how to make appointment can be found on the program page.

Data Science Major Program Committee

  • Tyler Caraza-Harter (Computer Sciences)
  • Michael Ferris (Computer Sciences)
  • B. Ian Hutchins (iSchool)
  • Bret Larget, Program Director (Statistics), committee chair
  • Sebastien Roch (Mathematics)
  • Sara Rodock (Statistics), advising representative

Helpful resources can be found at scholarships and Wisconsin Scholarship Hub. Additional information specific to Data Science students can be found on our major webpage and opportunities are regularly sent to declared students via our weekly newsletter.