MARC details
| 000 -LEADER |
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| fixed length control field |
05005nam a2200301 i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
EBL4334745 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
AU-PeEL |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20200220155822.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
170801s2016 njuf s 001 | eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781119153658 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
MIUC |
| Language of cataloging |
eng |
| Transcribing agency |
MIUC |
| 082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
658.8342 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
3209 |
| Personal name |
Siegel, Eric, |
| Dates associated with a name |
1968- |
| 952 ## - Items |
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| Itemnumber |
1776 |
| 245 10 - TITLE STATEMENT |
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| Title |
Predictive analytics : |
| Remainder of title |
the power to predict who will click, buy, lie, or die |
| Medium |
[electronic resource] / |
| 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 : |
| Name of publisher, distributor, etc. |
Wiley, |
| Date of publication, distribution, etc. |
2016. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 online resource (xxxi, 332 p., 20 unnumbered p. of plates) : |
| Other physical details |
ill. b&w. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Revised edition of the author's Predictive analytics, 2013. |
| -- |
Includes index. |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Introduction: The Prediction Effect -- <br/>Ch. 1. Liftoff! Prediction Takes Action (deployment) -- <br/>Ch. 2. With Power Comes Responsibility: Hewlett-Packard, Target, the Cops, and the NSA Deduce Your Secrets (ethics) -- <br/>Ch. 3. The Data Effect: A Glut at the End of the Rainbow (data) -- <br/>Ch. 4. The Machine That Learns: A Look Inside Chase's Prediction of Mortgage Risk (modeling) -- <br/>Ch. 5. The Ensemble Effect: Netflix, Crowdsourcing, and Supercharging Prediction (ensembles) -- <br/>Ch. 6. Watson and the Jeopardy! Challenge (question answering) -- <br/>Ch. 7. Persuasion by the Numbers: How Telenor, U.S. Bank, and the Obama Campaign Engineered Influence (uplift) -- <br/>Afterword -- <br/>Eleven Predictions for the First Hour of 2022 -- <br/>Appendices -- <br/>A. The Five Effects of Prediction -- <br/>B. Twenty Applications of Predictive Analytics -- <br/>C. Prediction People — "Cast of "Characters". |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die.<br/><br/>Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections.<br/><br/>How? Prediction is powered by the world's most potent, flourishing 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.<br/><br/>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 drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.<br/><br/>In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction:<br/>- What type of mortgage risk Chase Bank predicted before the recession<br/>- Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves<br/>- Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights<br/>- Five reasons why organizations predict death — including one health insurance company.<br/>- How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual.<br/>- Why the NSA wants all your data: machine learning supercomputers to fight terrorism.<br/>- How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy!<br/>- How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job.<br/>- How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison.<br/>- 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. <br/><br/>How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more.<br/><br/>A truly omnipresent science, predictive analytics constantly affects our daily lives. 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 |
|---|
| 9 (RLIN) |
334 |
| Topical term or geographic name entry element |
Economic forecasting |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
3210 |
| Topical term or geographic name entry element |
Prediction (Psychology) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
2634 |
| Topical term or geographic name entry element |
Human behavior |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
438 |
| Topical term or geographic name entry element |
Consumer behavior |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
http://ebookcentral.proquest.com/lib/miu/detail.action?docID=4334745 |
| Public note |
Click here to view |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Electronic resources |
