Students in the data science certificate will develop abilities such as data management, reproducibility, modeling strategies, and ethical considerations of data science to be paired with their knowledge gained from their major or domain area.  The certificate is a great fit for students who like programming, want to learn data analysis, and seek to be high-end users of data science tools in domain areas.  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

Students are eligible to declare the certificate at any point in their studies, however they should declare it as early as possible to plan the required coursework. See the departmental website for information about how to declare.

Students declared in the Data Science major or the Certificate in Engineering Data Analytics are not eligible to declare the Certificate in Data Science.


The certificate requires a minimum of 16 credits. 

Foundation Courses10-12
Complete two programming courses from7-8
Data Science Programming I 1
Data Science Programming II
Data Science Modeling I
Data Science & Engineering
Complete one ethics course from3-4
Data and Algorithms: Ethics and Policy (4-credit Communication B optional)
Ethics of Data for Engineers
Elective Courses6
Complete a minimum of 6 credits of electives, including at least 3 credits from the Fundamental Electives list.
Fundamental Electives3-6
Evolution, Ecology, and Genetics Laboratory
Cellular Biology Laboratory
Principles of Physiology Laboratory
Data Science Programming II 1
Matrix Methods in Machine Learning
Introduction to Big Data Systems
Introduction to Data Visualization
Introduction to Bioinformatics
Data Visualization for Economists
Introduction to Applied Econometrics
Introductory Econometrics
Economic Forecasting
Fundamentals of Data Analytics for Economists
Topics in Economic Data Analysis
Quantitative Ethnography
Data Analytics for Finance
Machine Learning for Business Analytics
Introduction to Geocomputing
Advanced Geocomputing and Geospatial Big Data Analytics
Geospatial Database Design and Development
GIS and Spatial Analysis
Fundamentals of Industrial Data Analytics
Machine Learning in Action for Industrial Engineers
Graphs and Networks in Data Science
Mathematical Methods in Data Science
Machine Learning in Physics
Statistics for Sociologists III
Using R for Soil and Environmental Sciences
Data Science Modeling II
Data Science Computing Project
Statistical Data Visualization
Introduction to Computational Statistics
Domain Electives0-3
Economic Decision Analysis
Computational Modeling of Biological Systems
Introduction to Optimization
Business Analytics II
Introduction to Databases
Navigating the Data Revolution: Concepts of Data & Information Science
Social Media Analytics
Data Analysis in Communications Research
Introduction to Survey Methods for Social Research
Social Network Analysis

Residence and Quality of Work

  • Minimum 2.000 GPA on all certificate courses
  • At least 9 credits must be taken in residence at UW-Madison 



COMP SCI 320 may count toward either the Foundation Courses or Fundamental Electives requirement, but not both.

Certificate Completion Requirement 

This undergraduate certificate must be completed concurrently with the student’s undergraduate degree. Students cannot delay degree completion to complete the certificate.

Learning Outcomes

  1. Apply tools and processes necessary for data management and reproducibility.
  2. Produce meaning from data employing modeling strategies.
  3. Learn best practices related to data science concepts and methods.
  4. Articulate policy, privacy, security and ethical considerations in data science projects.

Advising and Careers

Looking for Data Science Advising?

Students who are interested in data science academic advising should check out the advising information on our website or send an email to dscert@stat.wisc.edu.

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.


Advising Staff

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

Data Science 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