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The 9 Pitfalls of Data Science


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Winner of the 2020 Prose Award for Excellence in Popular Science & Popular Mathematics by the Association of American Publishers

About the Author

Gary Smith is the Fletcher Jones Professor of Economics at Pomona College. He received his Ph.D. in Economics from Yale University and was an Assistant Professor there for seven years. He has won two teaching awards and written (or co-authored) more than 80 academic papers and twelve books including Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie With Statistics, What the Luck? The Surprising Role of Chance in Our Everyday Lives, and Money Machine: The Surprisingly Simple Power of Value Investing. His research has been featured by Bloomberg Radio Network, CNBC, The Brian Lehrer Show, Forbes, The New York Times, Wall Street Journal, Motley Fool, Newsweek, and BusinessWeek. Jay Cordes is a data scientist who enjoys tackling challenging problems, including how to guide future data scientists away from the common pitfalls he saw in the corporate world. He's a recent graduate from UC Berkeley's Master of Information and Data Science (MIDS) program and graduated from Pomona College with a mathematics major. He has worked as a software developer and a data analyst and was also a strategic advisor and sparring partner for the winning pokerbot in the 2007 AAAI Computer Poker Competition world championship.


Gary Smith and Jay Cordes have a most captivating way and special talent to describe how easy it is to be fooled by the promises of spurious data and by the hype of data science. * Professor John P.A. Ioannidis, Scientist, Stanford University, "the godfather of science reform" (Wired), "one of the most influential scientists alive" (Atlantic) *
Using fascinating personal anecdotes and eye-opening historical accounts, Smith and Cordes guide us through interesting accounts of the prairie dog holes of data analysis where the unexperienced often break their ankles. I read it in two sittings. * Robert J. Marks II, Ph.D., Distinguished Professor of Electrical & Computer Engineering, Baylor University, Director, The Walter Bradley Center for Natural & Artificial Intelligence *
Smith and Cordes have produced a remarkably lucid, example-driven text that anybody working near data would do well to read. Though the book is presented as fables and pitfalls, a cogent, scientific approach reveals itself. Managers of data science teams stand to learn a great deal; seasoned data scientists will nod their heads knowingly. * D. Alex Hughes, Adjunct Assistant Professor, UC Berkeley School of Information *
Whether you manage a data science team or rely on data science to make critical decisions, this book illustrates how easy it is to draw wrong conclusions that appear to be supported by data. Gary Smith and Jay Cordes have written this must-read book for anyone who wants to avoid falling victim to the pitfalls, and make data-driven decisions with confidence. * Bill Chui, Director, GrandCare Health Services *
The current AI hype can be disorienting, but this refreshing book informs to realign expectations, and provides entertaining and relevant narrative examples that illustrate what can go wrong when you ignore the pitfalls of data science. Responsible data scientists should take heed of Smith and Cordes' guidance, especially when considering usingAI in healthcare where transparency about safety, efficacy, and equity is life-saving. * Michael Abramoff, MD, PhD, Founder and CEO of Idx, Watzke Professor of Ophthalmology and Visual Sciences at the University of Iowa *
In this era of big data, it's good to have a book that collects ways that big data can lie and mislead. This book provides practical advice for users of big data in a way that's easy to digest and appreciate, and will help guide them so that they can avoid its pitfalls. * Joseph Halpern, Joseph C. Ford Professor of Engineering, Computer Science Department, Cornell University *
Increasingly, the world is immersed in data! Gary Smith and Jay Cordes offer up a veritable firehose of fabulous examples of the uses/misuses of all that "big data" in real life. You will be a more informed citizen and better-armed consumer by reading their book... and, it couldn't come at a better time! * Shecky Riemann, math blogger *
An excellent guide to what might go wrong as more and more businesses embrace data-driven decision-making. * Avi Goldfarb, author of Prediction Machines *
The 9 Pitfalls of Data Science is the modern version of the classic book, How to Lie with Statistics. The authors write with authority, experience, and humor and makes for a very enjoyable and informative reading experience. * Arthur Benjamin, Professor of Mathematics, Harvey Mudd College, Author of The Magic of Math: Solving for X and Figuring Out Why *

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