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The 9 Pitfalls of Data Science
The 9 Pitfalls of Data Science

The 9 Pitfalls of Data Science

by Gary Smith - Jay Cordes
pubblicato da Oxford University Press

25,75
Disponibile in 1-2 settimane.
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Data science has never had more influence on the world. Large companies are now seeing the benefit of employing data scientists to interpret the vast amounts of data that now exists. However, the field is so new and is evolving so rapidly that the analysis produced can be haphazard at best. The 9 Pitfalls of Data Science shows us real-world examples of what can go wrong. Written to be an entertaining read, this invaluable guide investigates the all too common mistakes of data scientists - who can be plagued by lazy thinking, whims, hunches, and prejudices - and indicates how they have been at the root of many disasters, including the Great Recession. Gary Smith and Jay Cordes emphasise how scientific rigor and critical thinking skills are indispensable in this age of Big Data, as machines often find meaningless patterns that can lead to dangerous false conclusions. The 9 Pitfalls of Data Science is loaded with entertaining tales of both successful and misguided approaches to interpreting data, both grand successes and epic failures. These cautionary tales will not only help data scientists be more effective, but also help the public distinguish between good and bad data science.

La nostra recensione

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 * An excellent guide to what might go wrong as more and more businesses embrace data-driven decision-making. * Avi Goldfarb, author of Prediction Machines * 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 * 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 * 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 * 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 * 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 * 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 * 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) *

Dettagli

Generi Languages & Reference » Directories , Science & Nature » Science-Reference , Computers & Web » Computer Science

Editore Oxford University Press

Formato Hardback

Pubblicato 08/07/2019

Pagine 272

Lingua Inglese

Isbn o codice id 9780198844396

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