Predictive analytics : (Record no. 1230)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 07096cam a2200349 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 18812738 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220317094342.0 |
| 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) |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | DLC |
| Language of cataloging | eng |
| Transcribing agency | KCST |
| Description conventions | rda |
| 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 |
| Item number | Si Pr |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Siegel, Eric, |
| Dates associated with a name | 1968- |
| 9 (RLIN) | 5663 |
| 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. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| 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 |
| 500 ## - GENERAL NOTE | |
| General note | Revised edition of the author's Predictive analytics, 2013. |
| General note | Includes index. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| 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 entry element | Social sciences |
| General subdivision | Forecasting. |
| 9 (RLIN) | 5664 |
| Topical term or geographic name entry element | Economic forecasting |
| 9 (RLIN) | 5665 |
| Topical term or geographic name entry element | Prediction (Psychology) |
| 9 (RLIN) | 5666 |
| Topical term or geographic name entry element | Social prediction. |
| 9 (RLIN) | 5667 |
| Topical term or geographic name entry element | Human behavior. |
| 9 (RLIN) | 5668 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | 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 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Book |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Permanent Location | Current Location | Date acquired | Full call number | Barcode | Date last seen | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| KCST Library | KCST Library | 2022-03-17 | 303.49 Si Pr | 1000001044 | 2022-03-17 | 3.85 | 2022-03-17 | Book |