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Capponii$91482673 997 $aUNISANNIO LEADER 04657nam 2200685 450 001 9910208959703321 005 20230912134216.0 010 $a1-118-93893-3 010 $a1-118-93674-4 035 $a(CKB)3710000000278825 035 $a(SSID)ssj0001289923 035 $a(PQKBManifestationID)11745254 035 $a(PQKBTitleCode)TC0001289923 035 $a(PQKBWorkID)11232114 035 $a(PQKB)10295504 035 $a(OCoLC)881387969 035 $a(DLC) 2014022935 035 $a(Au-PeEL)EBL1757962 035 $a(CaPaEBR)ebr10902081 035 $a(CaONFJC)MIL633697 035 $a(CaSebORM)9781118938928 035 $a(MiAaPQ)EBC1757962 035 $a(MiAaPQ)EBC4040030 035 $a(EXLCZ)993710000000278825 100 $a20140815h20142014 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aUnderstanding the predictive analytics lifecycle /$fAlberto Cordoba 205 $a1st edition 210 1$aHoboken, New Jersey :$cWiley,$d2014. 210 4$d2014 215 $a1 online resource (240 pages) 225 1 $aWiley & SAS Business Series 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-118-86710-6 311 $a1-118-93892-5 320 $aIncludes bibliographical references and index. 327 $aMachine generated contents note: Foreword Preface Acknowledgments Chapter 1 Problem Identification and Definition Importance of Clear Business Objectives Office Politics Note Chapter 2 Design and Build The Managing Phase The Planning Phase The Delivery Phase Notes Chapter 3 Data Acquisition Data: the Fuel for Analytics A Data Scientist's Job Notes Chapter 4 Exploration and Reporting Visualization Cloud Reporting Chapter 5 Modeling Churn Model Risk Scoring Model Notes Chapter 6 Actionable Analytics Digital Asset Management Social Media Chapter 7 Feedback What the Different Software Components Should Do Note Conclusion Appendix: Useful Questions Bibliography About the Author Index . 330 $a"A high-level, informal look at the different stages of the predictive analytics cycleUnderstanding the Predictive Analytics Lifecycle covers each phase of the development of a predictive analytics initiative. Through the use of illuminating case studies across a range of industries that include banking, megaresorts, mobile operators, healthcare, manufacturing, and retail, the book successfully illustrates each phase of the predictive analytics cycle to create a playbook for future projects.Predictive business analytics involves a wide variety of inputs that include individuals' skills, technologies, tools, and processes. To create a successful analytics program or project to gain forward-looking insight into making business decisions and actions, all of these factors must properly align. The book focuses on developing new insights and understanding business performance based on extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management as input for human decisions. The book includes: An overview of all relevant phases: design, prepare, explore, model, communicate, and measure Coverage of the stages of the predictive analytics cycle across different industries and countries A chapter dedicated to each of the phases of the development of a predictive initiative A comprehensive overview of the entire analytic process lifecycle If you're an executive looking to understand the predictive analytics lifecycle, this is a must-read resource and reference guide"--$cProvided by publisher. 330 $a"Covers each phase of the development of a predictive analytics initiative. Through the use of case studies across a range of industries that include banking, megaresorts, mobile operators, healthcare, manufacturing, and retail, the book illustrates each phase of the predictive analytics cycle to create a playbook for future projects"--$cProvided by publisher. 410 0$aWiley and SAS business series. 606 $aDecision making$xStatistical methods 606 $aForecasting$xMathematical models 606 $aBusiness planning 615 0$aDecision making$xStatistical methods. 615 0$aForecasting$xMathematical models. 615 0$aBusiness planning. 676 $a658.4/013 686 $aBUS019000$2bisacsh 700 $aCordoba$b Alberto$f1958-$0884143 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910208959703321 996 $aUnderstanding the predictive analytics lifecycle$91974403 997 $aUNINA