About the Authors

This material has been used as the basis for many courses that Prof. Provost has introduced at New York University over the past two decades, including top-rated MBA courses, the Intro course for NYU’s MS in Data Science, and the intro course for NYU’s MS in Business Analytics.  The book provides examples of real-world business problems to illustrate the fundamental principles of data science.

Foster Provost

Foster Provost is Ira Rennert Professor of Entrepreneurship and Information Systems, Director Fubon Center, Data Analytics and AI, at NYU’s Stern School of Business, and Professor of Data Science and former Director of NYU’s Center for Data Science.  Prof. Provost is also a Distinguished Scientist at the real estate unicorn Compass.

In the 1990s, Prof. Provost worked as a data scientist for what’s now Verizon for five years, winning a President’s Award for his work there.

He was Editor-in-Chief of the journal Machine Learning from 2004 to 2010 and was Program Chair of the premier data science conference in 2001. He has collaborated with and advised many well-known companies, and he has founded several data-science based companies, including Detectica, Integral Ad Science, and Dstillery.

Professor Provost’s work has won many awards (among others), including most recently the ACM SIGKDD Test of Time Award for ground-breaking work on integrating crowd-sourcing and machine learning.

Foster Provost
Tom Fawcett

Tom Fawcett

Sadly, Tom passed away in a freak bicycle accident in 2020.

Previously, Tom Fawcett was a data scientist at Apple, and before that a Principal Data Scientist at Silicon Valley Data Science. He was an active member of the machine learning and data mining communities. He held a Ph.D. in machine learning from UMass-Amherst and had worked in industrial research (GTE Laboratories, NYNEX/Verizon Labs, HP Labs, etc.).

Over his career he published numerous conference and journal papers in machine learning. He completed a five-year term as action editor of the Machine Learning journal, before which he was an editorial board member. In 2003 he co-chaired the program of the premier machine learning conference (ICML) and has organized many workshops and journal special issues.

He received a Best Paper Award from KDD, a SCOPUS Award (most cited paper) from Pattern Recognition Letters, and a President’s Award from Verizon. As Tom would say, he frequently appears in print as “et al.”