MARC details
| 000 -LEADER |
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| fixed length control field |
04763cam a2200553 a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
808413896 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
AUA.1 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20200218112241.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
121231s2013 njua 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
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| LC control number |
2012047252 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781118356852 (cloth) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
1118356853 (cloth) |
| 024 8# - OTHER STANDARD IDENTIFIER |
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| Standard number or code |
40021986398 |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(OCoLC)808413896 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
DLC |
| Language of cataloging |
eng |
| Transcribing agency |
DLC |
| Modifying agency |
BTCTA |
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BDX |
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OCLCO |
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YDXCP |
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SINLB |
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DAD |
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YUS |
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A7U |
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OCLCO |
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IAD |
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NYP |
| 042 ## - AUTHENTICATION CODE |
|---|
| Authentication code |
pcc |
| 049 ## - LOCAL HOLDINGS (OCLC) |
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| Holding library |
NYPP |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
H61.4 |
| Item number |
.S54 2013 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
303.49 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Siegel, Eric. |
| 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. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Hoboken, N.J. : |
| Name of publisher, distributor, etc |
Wiley and sons Inc., |
| Date of publication, distribution, etc |
c2013. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvii, 302 p. : |
| Other physical details |
ill. ; |
| Dimensions |
24 cm. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Includes index. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references (p. 228-289) and index. |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Liftoff! prediction takes action : the stock market -- With power comes responsibility : HP, Target and the police deduce your secrets -- The data effect : blogger anxiety and the economy -- The machine that learns : Chase and mortgage risk. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
You have been predicted -- by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales. How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future -- lifting a bit of the fog off our hazy view of tomorrow -- means pay dirt. In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves. Why early retirement decreases life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death, including one health insurance company. How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths -- how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free. What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward -- but that can be predicted in advance? Whether you are a consumer of it -- or consumed by it -- get a handle on the power of Predictive Analytics. |
| 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. |
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| Put command parameter |
H61.4 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Delbert W. Baker Special Leadership Collection |
| Classification part |
H 61.4 |
| Item part |
.S54 2013 |
| 945 ## - LOCAL PROCESSING INFORMATION (OCLC) |
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