Study Data Science
Data Science is the fastest growing major at UW-Madison. Whether you are interested in a data science career, course, or workshop, opportunities abound here! Learn about undergraduate majors, graduate programs, the data science certificate, workshops, and internships and careers.
Explore Campus Resources
Data science has applications in all disciplines, and Data Science @ UW is your connection to data science institutes, centers, and programs across the UW-Madison campus. It’s also the place to find research funding and resources, coding meetups, seminars and events, data science faculty and communities of practice, student organizations, and more!
Collaborate With Us
Data science innovation at UW-Madison furthers the Wisconsin Idea by fueling discovery and economic development in all corners of the state and beyond. We are committed to fostering an inclusive culture in data science. Campus, industry, and community partners can benefit from our data science services.
Data Science Events
- March
- March 17"Vision-Language Models for Radiology AI" by Dr. Akshay Chaudhari, PhDML4MI Seminar Series10:00 AM, Online
- March 19Research Bazaar Data Science Research Bazaar: AI and ML in Research: Navigating Opportunities and Boundaries9:00 AM, Discovery Building
- March 19
News and Announcements
UW Research Partnership Yields First-of-its-Kind Soil Data Algorithm
University of Wisconsin professor of soil science Jingyi Huang and data scientist Maria Oros developed a new modeling tool for soil scientists. The pair used machine learning and public data to build the Soil Organic Carbon Assistant, which models changes in soil organic carbon.
Explore Potential and Limits of AI and ML at the Research Bazaar
Calling all data enthusiasts! Get ready to dive into the world of AI and machine learning at the 6th annual Data Science Research Bazaar, hosted by the Data Science Hub on March 19-20.
Submit a Nomination for the Inaugural UW-Madison Open Awards
The UW–Madison Open Awards recognize and celebrate those using open practices in their work and who are inspiring others to do the same.
The Ethics of Conservation Genetics
A team of researchers at UW–Madison is exploring intersections between conservation, genetics, ethics, and data science. The big picture is to lay the groundwork for technically and ethically sound research for all future conservation genetics work — and learn which conditions can make that work possible.
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Faces of Data Science
“Political science questions and data are inherently and inextricably related to statistics and empirical approaches. This is a major reason why UW–Madison is such an exciting place to work. The campus is overflowing with social science scholars and data-driven scientists who are incredibly open-minded to cross-disciplinary collaboration.”
Adeline Lo
Assistant Professor, Department of Political Science; Glenn B. & Cleone Orr Hawkins Chair
Adeline Lo studies how conflict and cooperation between groups impacts politics, especially the politics of migration. She developed her passion for this work at the end of college, during an internship with the International Rescue Committee. Interviewing asylum applicants and building cases for their resettlement in the U.S. showed her how much there is to learn about the political and socioeconomic factors surrounding migration.
Hearing migrants’ stories and taking on their perspectives can warm people to newcomers. Lo studies the impacts of these kinds of interventions, as well as the media’s representation of refugees. Her methods combine data science techniques for analyzing media data, such as convolutional neural networks for TV images, with randomized field projects and surveys. Through this work, she has learned that people’s emotional responses to interventions are important for their success.
Lo designs statistical tools to work with “odd, complicated, and messy” data, and she has created open-source R packages for social scientists. In recent decades, there has been a concerted push to make scientific research as transparent and replicable as possible. Lo believes that open-source resources such as computing and analysis packages can ease the effort of interrogating data and improving on existing work.