Study Data Science
Data Science is the fastest growing major at UW-Madison, and students across campus are interested in increasing their data literacy. Whether you are interested in a data science career or course, opportunities abound at UW-Madison. Learn more about majors, courses, and professional degrees and certificates
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, training, coding meetups, news, events and seminars, student orgs, communities of practice, and other resources.
Collaborate With Us
Data science innovation at UW-Madison furthers the Wisconsin Idea by fueling discovery and economic development. Our research and talent development benefit communities and businesses in all corners of the state, and beyond. We are committed to fostering an inclusive culture in data science. Learn about collaboration opportunities
Meet the Data Science Institute's New Director, Kyle Cranmer
Kyle Cranmer is the David R. Anderson Director of the American Family Insurance Data Science Institute. Cranmer leads the institute’s charge: advancing discoveries that benefit society through foundational and use-inspired data science research. He is a professor in the Physics Department with affiliate appointments in Computer Sciences and Statistics. One of Cranmer’s goals is to broaden engagement in data science across the UW-Madison campus. He aims to build partnerships between people working on data science methodology and those working in the natural, physical, and social sciences and the humanities. Read more
Faces of Data Science
“If we just rely on historic data, sometimes we might be carrying the biases that were in that data, and then there is a real problem of that getting amplified more and more when we train algorithms using that data. My hope is that we create environments where experts from different domains, as well as stakeholders, can come together to address these questions.”
Ramya Korlakai Vinayak
Assistant Professor, Departments of Electrical and Computer Engineering and Computer Sciences
Mathematics—and its potential to increase our understanding of how the world works—has always fascinated Ramya Korlakai Vinayak. In grad school, this enthusiasm for math, combined with her curiosity about the use of data and algorithms for making predictions, scientific discoveries, and decisions that benefit society, piqued her interest in machine learning.
Ramya is interested in data that either comes from, or is about, people. Her research focuses both on algorithms and query design for learning from this data. The data used to train algorithms for machine learning is commonly prepared through a process called labelling, where it is tagged with descriptive information. When people label data, they often make mistakes, and their errors and biases are amplified in machine learning models. Ramya has received support through the American Family Funding Initiative to develop methods for providing high-quality, affordable training data for specialized machine learning applications in industry.
Ramya collaborates with domain experts in her work, with the goal of producing algorithms that capture the variability in large, diverse populations. She stresses the importance of ensuring that people from different demographic backgrounds are represented in data. Ideally, this results in better machine learning models and inferences, leading to better decisions and policies in sectors like healthcare that impact people’s lives.
Data science is an emerging field that is roughly defined as the study, development, and application of methods that reveal new insights from data, which include numbers, text, images, graphs, sounds, code, and metadata. This website is an inclusive portal to resources and activities related to data science at UW-Madison.
As data reshapes our world, UW–Madison brings the power of data science to every field of study. The future path of data science will be shaped by insights from all disciplines.