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.
Requirements
The certificate requires a minimum of 16 credits.
| Code | Title | Credits |
|---|---|---|
| Foundation Courses | 10-12 | |
| Complete two programming courses from | 7-8 | |
| Data Science Programming I 1 | ||
or COMP SCI 320 | Data Science Programming II | |
| Data Science Modeling I | ||
| Data Science & Engineering | ||
| Complete one ethics course from | 3-4 | |
| Data and Algorithms: Ethics and Policy (4-credit Communication B optional) | ||
| Ethics of Data for Engineers | ||
| Elective Courses | 6 | |
Complete a minimum of 6 credits of electives, including at least 3 credits from the Fundamental Electives list. | ||
| Fundamental Electives | 3-6 | |
| 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 | ||
| Foundations of Statistical 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 Electives | 0-3 | |
| Economic Decision Analysis | ||
BIOCHEM 570 | ||
| 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
Footnotes
- 1
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
- Apply tools and processes necessary for data management and reproducibility.
- Produce meaning from data employing modeling strategies.
- Learn best practices related to data science concepts and methods.
- Articulate policy, privacy, security and ethical considerations in data science projects.
Advising and Careers
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 areas of jobs in the United States and in Wisconsin. The Occupational Outlook Handbook (OOH) from the Bureau of Labor Statistics shows the job growth outlook from 2023-33 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.
SuccessWorks
SuccessWorks at the College of Letters & Science helps you turn the academic skills learned in your classes into a fulfilling life, guiding you every step of the way to securing jobs, internships, or admission to graduate school.
Through one-on-one career advising, events, and resources, you can explore career options, build valuable internship and research experience, and connect with supportive alumni and employers who open doors of opportunity.
- What you can do with your major (Major Skills & Outcomes Sheets)
- Make a career advising appointment
- Learn about internships and internship funding
- Try “Jobs, Internships, & How to Get Them,” an interactive guide in Canvas for enrolled UW–Madison students