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Predictive and simulation analytics ...

Paczkowski, Walter R.

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Predictive and simulation analytics = deeper insights for better business decisions /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predictive and simulation analytics/ by Walter R. Paczkowski.
Reminder of title:	deeper insights for better business decisions /
Author:	Paczkowski, Walter R.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xxv, 370 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Decision making - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-3-031-31887-0
ISBN:	9783031318870

Predictive and simulation analytics = deeper 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

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

565918
Decision making
--Mathematical models.

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

Dewey Class. No.: 658.4033

Predictive and simulation analytics = deeper insights for better business decisions /
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505 0 $a 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.
520 $a 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.
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