Predictive analytics : (Record no. 75229)

000 -LEADER
fixed length control field 07824cam a2200541 i 4500
001 - CONTROL NUMBER
control field 18812738
003 - CONTROL NUMBER IDENTIFIER
control field CITU
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220905084309.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 151008s2016 njuf 001 0 eng
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number 2015031895
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781119145677 (paperback)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1119145678
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781119145684
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1119145686
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781119153658
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1119153654
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
042 ## - AUTHENTICATION CODE
Authentication code pcc
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number H61.4
Item number .S54 2016
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 303.49
Edition number 23
084 ## - OTHER CLASSIFICATION NUMBER
Classification number BUS016000
-- BUS021000
-- BUS043000
Source of number bisacsh
100 1# - MAIN ENTRY--PERSONAL NAME
Preferred name for the person Siegel, Eric,
Dates associated with a name 1968-
Relator term author
245 10 - TITLE STATEMENT
Title Predictive analytics :
Remainder of title the power to predict who will click, buy, lie, or die /
Statement of responsibility, etc Eric Siegel.
250 ## - EDITION STATEMENT
Edition statement Revised and Updated Edition.
264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Hoboken, New Jersey :
Name of publisher, distributor, etc Wiley,
Date of publication, distribution, etc 2016.
300 ## - PHYSICAL DESCRIPTION
Extent xxxi, 332 pages, 20 unnumbered pages of plates ;
Dimensions 23 cm
336 ## - CONTENT TYPE
Content type term text
Source rdacontent
Content type code txt
337 ## - MEDIA TYPE
Media type term unmediated
Source rdamedia
Media type code n
338 ## - CARRIER TYPE
Carrier type term volume
Source rdacarrier
Carrier type code nc
500 ## - GENERAL NOTE
General note Revised edition of the author's Predictive analytics, 2013.
500 ## - GENERAL NOTE
General note Includes index.
505 8# - CONTENTS
Formatted contents note Machine generated contents note: Foreword Thomas H. Davenport xiii 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 xv What is the occupational hazard of predictive analytics? Introduction The Prediction Effect 1 How does predicting human behavior combat risk, fortify healthcare, toughen crime fighting, and boost sales? Why must a computer learn in order to predict? How can lousy predictions be extremely valuable? What makes data exceptionally exciting? How is data science like porn? Why shouldn't computers be called computers? Why do organizations predict when you will die? Chapter 1 Liftoff! Prediction Takes Action (deployment) 17 How much guts does it take to deploy a predictive model into field operation, and what do you stand to gain? What happens when a man invests his entire life savings into his own predictive stock market trading system? Chapter 2 With Power Comes Responsibility: Hewlett-Packard, Target, the Cops, and the NSA Deduce Your Secrets (ethics) 37 How do we safely harness a predictive machine that can foresee job resignation, pregnancy, and crime? Are civil liberties at risk? Why does one leading health insurance company predict policyholder death? Two extended sidebars reveal: 1) Does the government undertake fraud detection more for its citizens or for self-preservation, and 2) for what compelling purpose does the NSA need your data even if you have no connection to crime whatsoever, and can the agency use machine learning supercomputers to fight terrorism without endangering human rights? Chapter 3 The Data E ffect: A Glut at the End of the Rainbow (data) 67 We are up to our ears in data. How much can this raw material really tell us? What actually makes it predictive? What are the most bizarre discoveries from data? When we find an interesting insight, why are we often better off not asking why? In what way is bigger data more dangerous? How do we avoid being fooled by random noise and ensure scientific discoveries are trustworthy? Chapter 4 The Machine That Learns: A Look Inside Chase's Prediction of Mortgage Risk (modeling) 103 What form of risk has the perfect disguise? How does prediction transform risk to opportunity? What should all businesses learn from insurance companies? Why does machine learning require art in addition to science? What kind of predictive model can be understood by everyone? How can we confidently trust a machine's predictions? Why couldn't prediction prevent the global financial crisis? Chapter 5 The Ensemble Effect: Netflix, Crowdsourcing, and Supercharging Prediction (ensembles) 133 To crowdsource predictive analytics--outsource it to the public at large--a company launches its strategy, data, and research discoveries into the public spotlight. How can this possibly help the company compete? What key innovation in predictive analytics has crowdsourcing helped develop? Must supercharging predictive precision involve overwhelming complexity, or is there an elegant solution? Is there wisdom in nonhuman crowds? Chapter 6 Watson and the Jeopardy! Challenge (question answering) 151 How does Watson--IBM's Jeopardy!-playing computer--work? Why does it need predictive modeling in order to answer questions, and what secret sauce empowers its high performance? How does the iPhone's Siri compare? Why is human language such a challenge for computers? Is artificial intelligence possible? Chapter 7 Persuasion by the Numbers: How Telenor, U.S. Bank, and the Obama Campaign Engineered Influence (uplift) 187 What is the scientific key to persuasion? Why does some marketing fiercely backfire? Why is human behavior the wrong thing to predict? What should all businesses learn about persuasion from presidential campaigns? What voter predictions helped Obama win in 2012 more than the detection of swing voters? How could doctors kill fewer patients inadvertently? How is a person like a quantum particle? Riddle: What often happens to you that cannot be perceived, and that you can't even be sure has happened afterward--but that can be predicted in advance? Afterword 218 Eleven Predictions for the First Hour of 2022 Appendices A. The Five Effects of Prediction 221 B. Twenty Applications of Predictive Analytics 222 C. Prediction People--Cast of "Characters" 225 Notes 228 Acknowledgments 290 About the Author 292 Index 293 .
520 ## - SUMMARY, ETC.
Summary, etc "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"--
Assigning source Provided by publisher.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Social sciences
General subdivision Forecasting.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Economic forecasting
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Prediction (Psychology)
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Social prediction.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Human behavior.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element BUSINESS & ECONOMICS / Consumer Behavior.
Source of heading or term bisacsh
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element BUSINESS & ECONOMICS / Econometrics.
Source of heading or term bisacsh
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element BUSINESS & ECONOMICS / Marketing / General.
Source of heading or term bisacsh
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Online version:
Main entry heading Siegel, Eric, 1968-
Title Predictive analytics
Edition Revised and Updated Edition.
Place, publisher, and date of publication Hoboken : Wiley, 2016
International Standard Book Number 9781119145684
Record control number (DLC) 2015039877
856 42 - ELECTRONIC LOCATION AND ACCESS
Materials specified Cover image
Uniform Resource Identifier http://catalogimages.wiley.com/images/db/jimages/9781119145677.jpg
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN)
a 7
b cbc
c orignew
d 1
e ecip
f 20
g y-gencatlg
942 ## - ADDED ENTRY ELEMENTS
Source of classification or shelving scheme
Item type BOOK
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Permanent Location Current Location Shelving location Date acquired Source of acquisition Cost, normal purchase price Inventory number Full call number Barcode Date last seen Price effective from Item type
          COLLEGE LIBRARY COLLEGE LIBRARY SUBJECT REFERENCE 2016-08-22   2016.00 47557 303.49 Si155 2016 CITU-CL-47557 2021-09-09 2021-09-09 BOOK