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|a 9783031318870
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| 100 |
1 |
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|a Paczkowski, Walter R.
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| 245 |
0 |
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|a Predictive and Simulation Analytics
|h Elektronische Ressource
|b Deeper Insights for Better Business Decisions
|c by Walter R. Paczkowski
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| 250 |
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|a 1st ed. 2023
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2023, 2023
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| 300 |
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|a XXV, 370 p. 173 illus., 149 illus. in color
|b online resource
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| 505 |
0 |
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|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
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| 653 |
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|a Statistics
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| 653 |
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|a Business Informatics
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| 653 |
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|a Statistics in Business, Management, Economics, Finance, Insurance
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| 653 |
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|a Business / Data processing
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| 653 |
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|a Business Analytics
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 028 |
5 |
0 |
|a 10.1007/978-3-031-31887-0
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-031-31887-0?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 300.727
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| 520 |
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|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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