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 set up an appointment with a data science major advisor prior to attaining senior standing (86 credits). There are no specific courses that must be completed before declaration.
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 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 Arts (B.A.)
Students pursuing a bachelor of arts 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 a bachelor of arts or a bachelor of science curriculum.
Bachelor of Arts degree requirements
|Mathematics||Complete the University General Education Requirements for Quantitative Reasoning A (QR-A) and Quantitative Reasoning B (QR-B) coursework.|
|Foreign Language|| |
|L&S Breadth|| |
|Liberal Arts and Science Coursework||Complete at least 108 credits.|
|Depth of Intermediate/Advanced work||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|| |
|Quality of Work|| |
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
|Foundational Math Courses|
|MATH 221||Calculus and Analytic Geometry 1||5|
|or MATH 217||Calculus with Algebra and Trigonometry II|
|or MATH 275||Topics in Calculus I|
|MATH 222||Calculus and Analytic Geometry 2||4|
|or MATH 276||Topics in Calculus II|
|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||3-4|
Students must complete at least one course from each of the four following categories and then additional electives to reach the minimum credits. Additional courses taken within each category may count towards other electives.
|Complete one of the following:|
|Matrix Methods in Machine Learning|
|Introduction to Artificial Neural Networks|
|Introduction to Artificial Intelligence|
|Machine Learning for Business Analytics|
|Mathematical Methods in Data Science|
|Introduction to Machine Learning and Statistical Pattern Classification|
|Introduction to Deep Learning and Generative Models|
|Complete one of the following:|
|Introduction to Numerical Methods|
|Introduction to Computational Statistics|
|Numerical Linear Algebra|
|Introduction to Optimization|
|Database Management Systems: Design and Implementation|
|Introduction to Bioinformatics|
|Advanced Geocomputing and Geospatial Big Data Analytics|
|Geospatial Database Design and Development|
|Complete one of the following:|
|Introduction to Applied Econometrics|
|Introduction to Probability and Mathematical Statistics I|
|Introduction to Probability and Mathematical Statistics II|
|Introduction to Theory and Methods of Mathematical Statistics I|
|Introduction to Theory and Methods of Mathematical Statistics II|
|Introduction to Time Series|
|Introductory Nonparametric Statistics|
|Applied Categorical Data Analysis|
|Statistical Experimental Design|
|Introduction to the Theory of Probability|
|Classification and Regression Trees|
|Applied Multivariate Analysis|
|Introduction to Stochastic Processes|
|An Introduction to Brownian Motion and Stochastic Calculus|
|Complete one from the following:|
|Linear Algebra and Differential Equations|
|Elementary Matrix and Linear Algebra|
|Topics in Multi-Variable Calculus and Linear Algebra|
|For additional electives students may complete courses from the list below or additional courses from the required categories above:|
|Introduction to Combinatorial Optimization|
|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|
|Graphic Design in Cartography|
|Interactive Cartography & Geovisualization|
|Operations Research-Deterministic Modeling|
|Fundamentals of Industrial Data Analytics|
|Inspection, Quality Control and Reliability|
|Introduction to Quality Engineering|
|Information Sensing and Analysis for Manufacturing Processes|
|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 with R|
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).
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.|
- 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.
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.
|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|
|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|
|L I S 461 (enroll in Communication B section)||3-4||Statistical Modeling course||3|
|Machine Learning course||3||Physical Science Breadth||3|
|Biological Science Breadth||3||Social Science Breadth||3|
|Social Science Breadth||3||Electives||6|
|Advanced Computing course||3||Data Science elective||3|
|Data Science elective||3||Electives||10|
|Social Science Breadth||3|
|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.
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
|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|
|L I S 461 (meets Humanities Breadth, 4cr section meets Communication B)||3-4||Machine Learning Course||3|
|Linear Algebra Course||3||Statistical Modeling Course||3|
|Biological Science Breadth||3||Literature Breadth||3|
|Social Science Breadth||3||Physical Science Breadth||3|
|Advanced Computing Course||3||Data Science Elective||3|
|Data Science Elective||3||Literature Breath||3|
|Science Breadth||3||Science Breadth||3|
|Social Science Breadth||6||Electives||6|
|Total Credits 90|
Looking for Data Science advising?
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 thousands of jobs, for example, in 2018 Data Scientist was the #1 job on the web site Glassdoor with over 25,000 jobs, Monster.com listed over 12,000 jobs in data science nationally, and Indeed.com had over 1,000 jobs for data analysts just in the state of Wisconsin.
Additionally, the Occupational Outlook Handbook (OOH) from the Bureau of Labor Statistics shows the job growth outlook from 2016-26 for Mathematicians and Statisticians to be 33% (much faster than average) and for Computer and Information Research Scientists to be 19% (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.
- Set up a career advising appointment
- Enroll in a Career Course - a great idea for first- and second-year students:
- Learn about internships and internship funding
- 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
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