LEADER 03894nam 22006975 450 001 9910735796403321 005 20260330180946.0 010 $a9783031318870 010 $a3031318870 024 7 $a10.1007/978-3-031-31887-0 035 $a(CKB)27670911200041 035 $a(MiAaPQ)EBC30654996 035 $a(Au-PeEL)EBL30654996 035 $a(DE-He213)978-3-031-31887-0 035 $a(PPN)272256471 035 $a(OCoLC)1390918504 035 $a(EXLCZ)9927670911200041 100 $a20230718d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPredictive and Simulation Analytics $eDeeper Insights for Better Business Decisions /$fby Walter R. Paczkowski 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (381 pages) 311 08$a9783031318863 327 $aPart 1: The Analytics Quest: The Drive for Rich Information -- 1. Decisions, Information, and Data -- 2. A Systems Perspective -- Part 2: Predictive Analytics: Background -- 3. Information Extraction: Basic Time Series Methods -- 4. Information Extraction: Advanced Time Series Methods -- 5. Information Extraction: Non-Time Series Methods -- 6. Useful Life of a Predictive Model -- Part 3: Simulation Analytics: Background -- 7. Introduction to Simulations -- 8. Designing and analyzing a Simulation -- 9. Random Numbers: The Backbone of Stochastic Simulations -- 10. Examples of Stochastic Simulations: Monte Carlo Simulations -- Part 4: Melding The Two Analytics -- 11. Melding Predictive and Simulation Analytics -- 12. Applications: Operational Scale-View -- 13. Applications: Tactical and Strategic Scale-Views. 330 $aThis book connects predictive analytics and simulation analytics, with the end goal of providing Rich Information to stakeholders in complex systems to direct data-driven decisions. Readers will explore methods for extracting information from data, work with simple and complex systems, and meld multiple forms of analytics for a more nuanced understanding of data science. The methods can be readily applied to business problems such as demand measurement and forecasting, predictive modeling, pricing analytics including elasticity estimation, customer satisfaction assessment, market research, new product development, and more. The book includes Python examples in Jupyter notebooks, available at the book's affiliated Github. This volume is intended for current and aspiring business data analysts, data scientists, and market research professionals, in both the private and public sectors. 606 $aStatistics 606 $aBusiness$xData processing 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aBusiness Analytics 606 $aBusiness Informatics 606 $aPresa de decisions$2thub 606 $aModels matemātics$2thub 606 $aDirecciķ d'empreses$2thub 606 $aEstadística matemātica$2thub 606 $aMatemātica discreta$2thub 608 $aLlibres electrōnics$2thub 615 0$aStatistics. 615 0$aBusiness$xData processing. 615 14$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aBusiness Analytics. 615 24$aBusiness Informatics. 615 7$aPresa de decisions 615 7$aModels matemātics 615 7$aDirecciķ d'empreses 615 7$aEstadística matemātica 615 7$aMatemātica discreta 676 $a658.4033 676 $a658.4033 700 $aPaczkowski$b Walter R$01074957 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910735796403321 996 $aPredictive and Simulation Analytics$93418732 997 $aUNINA