Affiliation: University of California, Berkeley

Educational Background: BA, Vassar College; PhD, Yale University

Deborah Nolan grew up in the northeast, moving every few years with her parents and four siblings to new homes in New York, Massachusetts, Pennsylvania, and Connecticut before heading to college. While a student at Vassar College, Nolan majored in pure mathematics. Her one exposure to statistics was a summer internship during which she helped analyze data from a survey in a women’s magazine about how family life had changed as more women became breadwinners. That experience convinced her to stay away from statistics and stick with the comfortable world of theoretical math and coding.

After graduating from college, Nolan worked for IBM as a programmer and learned how to code in several languages, from low-level machine code to scripting languages. But her work as an applications programmer brought her back to the world of statistics, where she helped market researchers analyze their data. Again, Nolan saw there was a lot more to statistics than formulas, and answering questions with data was hard. This time, though, she was drawn to the challenge and started graduate school at Yale University to learn more about the statistics field. At Yale, she drifted back to pure mathematics and studied the theoretical behavior of an empirical process based on U-statistics.

At the University of California, Berkeley, Nolan developed her love of teaching and was the recipient of several teaching awards, including the ASA Waller Distinguished Teaching Career Award and Berkley’s Distinguished Teaching Award. Her previous experience ‘practicing’ statistics taught her the importance of learning statistics through case studies, where the methodologies and approaches develop out of an understanding of the data, context, and question to be addressed. Nolan has co-authored several case-based data science and statistics books with this goal in mind, and she has designed and run several summer programs to introduce students to the excitement of the statistics and data science fields through exploration and visualization.

For Nolan, data science was a natural next step from the satisfaction she found working on problems in pure math, making her way through coding challenges, and investigating data. After co-developing her first data science course, Concepts in Computing with Data, she co-designed the data science major at Berkeley and co-developed a core technical course for the major, Principles and Techniques for Data Science. Today, nearly 1,000 students graduate each year with a data science major from Berkeley, and the creation of the first new college at Berkeley in 50 years—the College of Computing, Data Science, and Society—can be traced directly to student demand for data science. Nolan is grateful for being the inaugural associate dean for students of this college.