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Predictive and simulation analyticsdeeper insights for better business decisions /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Predictive and simulation analyticsby Walter R. Paczkowski.
其他題名:	deeper insights for better business decisions /
作者:	Paczkowski, Walter R.
出版者:	Cham :Springer International Publishing :2023.
面頁冊數:	xxv, 370 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Decision makingMathematical models.
電子資源:	https://doi.org/10.1007/978-3-031-31887-0
ISBN:	9783031318870$q(electronic bk.)

Predictive and simulation analyticsdeeper insights for better business decisions /
Paczkowski, Walter R.

Predictive and simulation analyticsdeeper insights for better business decisions /[electronic resource] :by Walter R. Paczkowski. - Cham :Springer International Publishing :2023. - xxv, 370 p. :ill., digital ;24 cm.

Part 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.

This 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.

ISBN: 9783031318870$q(electronic bk.)

Standard No.: 10.1007/978-3-031-31887-0doiSubjects--Topical Terms:

190791
Decision making
--Mathematical models.

LC Class. No.: HD30.23 / .P33 2023

Dewey Class. No.: 658.4033

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