000			04014cam a2200409 i 4500
020			_a9781119153658 (epub)
020			_a1119153654 (epub)
020			_a9781119145684 (pdf)
020			_a1119145686 (pdf)
020			_z9781119145677 (paperback)
020			_a9781119172536
020			_a1119172535
020			_a1119145678 (paperback)
020			_a9781119145677 (paperback)
037			_aC0E318E3-3943-49AE-9C59-22C347F03AD7 _bOverDrive, Inc. _nhttp://www.overdrive.com
040			_cCUS
072		7	_aSOC _x000000 _2bisacsh
084			_aBUS016000 _aBUS021000 _aBUS043000 _2bisacsh
100	1		_aSiegel, Eric, _d1968-
245	1	0	_aPredictive analytics : _bthe power to predict who will click, buy, lie, or die / _cEric Siegel.
260			_aRevised and Updated Edition.
260		1	_aHoboken : _bWiley, _c2016.
300			_a1 online resource.
500			_aRevised edition of the author's Predictive analytics, 2013.
500			_aIncludes index.
520			_a"Predictive analytics unleashes the power of data. With this technology, computers literally learn from data how to predict future behaviors of individuals. In this updated and revised edition of Predictive Analytics, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction. New material includes: - The Real Reason the NSA Wants Your Data: Automatic Suspect Discovery. A special sidebar in Chapter 2, "With Power Comes Responsibility," presumes--with much evidence--that the National Security Agency considers PA a strategic priority. Can the organization use PA without endangering civil liberties? - Dozens of new examples from Facebook, Hopper, Shell, Uber, UPS, the U.S. government, and more. The Central Tables' compendium of mini-case studies has grown to 182 entries, including breaking examples. - A much needed warning regarding bad science. Chapter 3, "The Data Effect," includes an in-depth section about an all-too-common pitfall, and how we avoid it, i.e., how to successfully tap data's potential without being fooled by random noise, ensuring sound discoveries are made. - Even more extensive Notes, updated and expanded to 70+ pages, now moved to an online PDF. Now located at www.predictivenotes.com, the Notes include citations and comments that cover the above new content, as well as new citations for many other topics"-- _cProvided by publisher.
505	0		_aForeword / Thomas H Davenport -- Preface to the Revised and Updated Edition: What's new and who's this book for-the predictive analytics FAQ -- Preface to the Original Edition: What is the occupational hazard of predictive analytics? -- Introduction: Prediction effect -- Liftoff! prediction takes action (deployment) -- With power comes responsibility: Hewlett-Packard, Target, the Cops, and the NSA deduce your secrets (ethics) -- Data effect: a glut at the end of the rainbow (data) -- Machine that learns: a look inside Chase's prediction of mortgage risk (modeling) -- Ensemble effect: Netflix, crowdsourcing, and supercharging prediction (ensembles) -- Watson and the jeopardy! challenge (question answering) -- Persuasion by the numbers: how Telenor, US Bank, and the Obama Campaign engineered influence (uplift) -- Afterword: Eleven predictions for the first hour of 2022 -- Appendices: -- A: Five effects of prediction -- B: Twenty applications of predictive analytics -- C: Prediction people: cast of "characters" -- Hands-On Guide: Resources for further leaning -- Acknowledgments -- About the author -- Index.
650		0	_aSocial sciences _xForecasting.
650		0	_aEconomic forecasting.
650		0	_aPrediction (Psychology)
650		0	_aSocial prediction.
650		0	_aHuman behavior.
650		7	_aBUSINESS & ECONOMICS / Consumer Behavior. _2bisacsh
650		7	_aBUSINESS & ECONOMICS / Econometrics. _2bisacsh
650		7	_aBUSINESS & ECONOMICS / Marketing / General. _2bisacsh
856	4	0	_uhttps://doi.org/10.1002/9781119172536 _zWiley Online Library
942			_cEBK
999			_c208662 _d208662