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.”
How to Get in
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 |
* 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 (BS)
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.
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. |
| Language | Complete the third unit of a language other than English. |
| LS 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 pursuing an L&S major
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
| Code | Title | Credits |
|---|---|---|
| Foundational Math Courses | ||
| MATH 221 | Calculus and Analytic Geometry 1 | 5 |
| or MATH 217 | Calculus with Algebra and Trigonometry II | |
| MATH 222 | Calculus and Analytic Geometry 2 | 4 |
| Total Credits | 9 | |
| Code | Title | Credits |
|---|---|---|
| Foundational Data Science Courses | ||
| STAT 240 | Data Science Modeling I | 4 |
| STAT 340 | Data Science Modeling II | 4 |
| COMP SCI 220 | Data Science Programming I | 4 |
| or COMP SCI 300 | Programming II | |
| COMP SCI 320 | Data Science Programming II | 4 |
| L I S 461 | Data and Algorithms: Ethics and Policy (4-credit Communication B section optional) | 3-4 |
| or E C E/I SY E 570 | Ethics of Data for Engineers | |
| Total Credits | 19-20 | |
| Code | Title | Credits |
|---|---|---|
| Electives | ||
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 Learning | 3 | |
| 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 | ||
| Machine Learning in Physics | ||
| Introduction to Machine Learning and Statistical Pattern Classification | ||
| Introduction to Deep Learning and Generative Models | ||
| Advanced Computing | 3 | |
| 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 Data Visualization | ||
| Introduction to Bioinformatics | ||
| Advanced Geocomputing and Geospatial Big Data Analytics | ||
| Geospatial Database Design and Development | ||
| Graphs and Networks in Data Science | ||
| Statistical Modeling | 3 | |
| 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 | ||
or STAT 311 | Introduction to Theory and Methods of Mathematical Statistics I | |
| Introduction to the Theory of Probability | ||
| Introduction to Probability and Mathematical Statistics II 2 | ||
or STAT 312 | 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 | ||
| Statistical Methods for Spatial Data | ||
| Probability Theory | ||
| Introduction to Stochastic Processes | ||
| An Introduction to Brownian Motion and Stochastic Calculus | ||
| Linear Algebra | 3 | |
| 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 Electives | 6 | |
| 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 | ||
| Navigating the Data Revolution: Concepts of Data & Information Science | ||
| Applied Database Design | ||
| Introduction to Text Mining | ||
| Social Media Analytics | ||
| Data Analysis in Communications Research | ||
| Introductory Probability 2 | ||
| Introduction to Survey Methods for Social Research | ||
| Social Network Analysis | ||
| Practicum in Analysis and Research | ||
| Using R for Soil and Environmental Sciences | ||
| Data Science Computing Project | ||
| Data Science with R | ||
| Total Credits | 18 | |
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
Footnotes
- 1
Upper-level in the major includes L I S 461 and all courses counting towards the Electives requirement (i.e. Machine Learning, Advanced Computing, Statistical Modeling, Linear Algebra, and Other Electives).
- 2
Students are only allowed to count one course from each of probability (MATH 331, 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. |
Learning Outcomes
- Integrate foundational concepts and tools from mathematics, computer science, and statistics to solve data science problems.
- Demonstrate competencies with tools and processes necessary for data management and reproducibility.
- Produce meaning from data employing modeling strategies.
- Demonstrate critical thinking related to data science concepts and methods.
- Conduct data science activities aware of and according to policy, privacy, security and ethical considerations.
- Demonstrate oral, written, and visual communication skills related to data science.
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.
| Freshman | |||
|---|---|---|---|
| Fall | Credits | Spring | Credits |
| COMP SCI 220 | 4 | COMP SCI 320 | 4 |
| Communication A | 3 | MATH 221 | 5 |
| Biological Science Breadth | 3 | Ethnic Studies | 3 |
| Foreign Language (if needed) | 4 | Foreign Language (if needed) | 4 |
| 14 | 16 | ||
| Sophomore | |||
| Fall | Credits | Spring | Credits |
| MATH 222 | 4 | STAT 340 | 4 |
| STAT 240 | 4 | Linear Algebra course | 3 |
| Literature Breadth | 3 | Humanities Breadth | 3 |
| Physical Science Breadth | 3 | Literature Breadth | 3 |
| INTER-LS 210 | 1 | Social Science Breadth | 3 |
| 15 | 16 | ||
| Junior | |||
| Fall | Credits | Spring | Credits |
| Advanced Computing course | 3 | Statistical Modeling course | 3 |
| Biological Science Breadth | 3 | Physical Science Breadth | 3 |
| Social Science Breadth | 3 | Social Science Breadth | 3 |
| Elective | 6 | Electives | 6 |
| 15 | 15 | ||
| Senior | |||
| Fall | Credits | Spring | Credits |
| L I S 461 (Meets Humanities breadth; 4-credit Communication B section optional) | 3-4 | Data Science elective | 3 |
| Machine Learning course | 3 | Data Science elective | 3 |
| Social Science Breadth | 3 | Electives | 7 |
| Electives | 6 | ||
| 15 | 14 | ||
| Total Credits 120 | |||
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 | |||
|---|---|---|---|
| Fall | Credits | Spring | Credits |
| STAT 240 | 4 | STAT 340 | 4 |
| COMP SCI 220 | 4 | COMP SCI 320 | 4 |
| Communications A complete during first year | 3 | Ethnics Studies complete within first 60 credits | 3 |
| Social Science Breadth | 3 | Humanities Breadth | 3 |
| 14 | 14 | ||
| Second Year | |||
| Fall | Credits | Spring | Credits |
| Linear Algebra Course | 3 | Advanced computing course | 3 |
| Statistical Modeling course | 3-4 | Data Science elective | 3 |
| Biological Science Breadth | 3 | Literature Breadth | 3 |
| Social Science Breadth | 3 | Physical Science Breadth | 3 |
| Elective | 3-4 | INTER-LS 210 | 1 |
| Elective | 3 | ||
| 15 | 16 | ||
| Third Year | |||
| Fall | Credits | Spring | Credits |
| L I S 461 (Meets Humanities breadth; 4-credit Communication B section optional) | 3-4 | Data Science Elective | 3 |
| Machine Learning course | 3 | Literature Breath | 3 |
| Science Breadth | 3 | Science Breadth | 3 |
| Social Science Breadth | 6 | Electives | 6 |
| 16 | 15 | ||
| Total Credits 90 | |||
Advising and Careers
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 areas 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 website 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.
Departmental Resources
- Data Science Skills Sheet, aka What you can do with your Data Science major
- Career Pathways for Statistics and Data Science Canvas Course
- Department of Statistics Student Career Resources webpage
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.
- SuccessWorks
- Set up a career advising appointment
- Enroll in a Career Course - a great idea for first- and second-year students:
- INTER-LS 210 L&S Career Development: Taking Initiative (1 credit)
- INTER-LS 215 Communicating About Careers (3 credits, fulfills Comm B General Education Requirement)
- Learn about internships and internship funding
- INTER-LS 260 Internship in the Liberal Arts and Sciences
- Activate your Handshake account to apply for jobs and internships from 200,000+ employers recruiting UW-Madison students
- Learn about the impact SuccessWorks has on students' lives
People
Advising Staff
Information regarding the Data Science advisors and how to make an 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
- Nan Chen (Mathematics)
- Sara Rodock (Statistics), advising representative
Resources and Scholarships
Helpful resources can be found at scholarships and the 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.