Deborah Nolan

Affiliation:
Department of Statistics, University of California

Educational Background:
BA, Mathematics, Vassar College
PhD, Statistics, Yale University

About Deborah
Deb grew up in the northeastern United States. Her family moved around a lot, living in New York, Massachusetts, Pennsylvania, and Connecticut before she headed to Vassar College, where she majored in mathematics. A New Year’s resolution four years later brought her to statistics.

She was working for IBM as an applications programmer, assisting market researchers with their customer data. Deb used SAS to massage the data into a form the market researchers could analyze. Unfortunately, they thought SAS could do everything, including figuring out how to analyze the data. This experience helped Deb appreciate the importance of statistics and motivated her to pursue formal study in statistics.

After a year of studying nights and weekends at Columbia, Deb decided to study full time. The decision happened on New Year’s Eve, when her boyfriend came down with the flu and their plans for a romantic celebration were nixed. That night, with a lot of quiet time on her hands, Deb realized she wanted to learn statistics more deeply and the best way to do that would be to go to graduate school full time. She admits to not being the type to make New Year’s resolutions, but she’s glad she did that night.

Deb has been recognized for excellence in teaching, including receiving the ASA’s Waller Distinguished Teaching Career Award. Her unique contribution to education has been through the connections she makes between statistics research/practice and education and between computing and statistics. Her book Stat Labs: Mathematical Statistics through Applications, with Terry Speed, aims to bring real-world statistical problems into courses. It departs significantly from traditional textbooks in its use of extensive case studies. And her book Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving, with Duncan Temple Lang, has a similar aim for data science. Most recently, her forthcoming book, Communicating with Data: The Art of Writing for Data Science, with Sara Stoudt, addresses the teaching and learning challenges of communicating the story behind a statistical analysis that is both compelling and faithful to the data.