Exploratory data analysis is an approach to examining data that emphasizes visually describing and interactively and iteratively inspecting data. EDA takes place prior to performing confirmatory statistical analysis such as conducting statistical tests or fitting statistical models. Only in recent years have tools that allow visual, flexible exploration of data become mature enough to make EDA available to a wide audience.
Marti Hearst is a professor in the School of Information at UC Berkeley with an affiliate appointment in the CS department. Her primary research interests are user interfaces for search engines, information visualization, natural language processing, and improving MOOCs. She wrote the first book on Search User Interfaces.
Prof. Hearst was named a Fellow of the ACM in 2013 and has received an NSF CAREER award, an IBM Faculty Award, two Google Research Awards, an Okawa Foundation Fellowship, three Excellence in Teaching Awards, and has been principal investigator for more than $3.5M in research grants.
Prof. Hearst has served on the Advisory Council of NSF’s CISE Directorate and is currently on the Web Board for CACM, member of the Usage Panel for the American Heritage Dictionary, and on the Edge.org panel of experts. She is on the editorial board of ACM Transactions on Computer-Human Interaction and was formerly on the boards of ACM Transactions on the Web, Computational Linguistics, ACM Transactions on Information Systems, and IEEE Intelligent Systems.
Prof. Hearst received BA, MS, and PhD degrees in computer science from the University of California at Berkeley, and she was a Member of the Research Staff at Xerox PARC from 1994 to 1997.