Overview

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.

Full Product Details

Author:   Walter R. Paczkowski
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2023 ed.
Weight:   0.758kg
ISBN:  

9783031318863

ISBN 10:   3031318862
Pages:   370
Publication Date:   20 July 2023
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

Reviews

“Throughout the book there are extensive references to websites, in the form of footnotes ... . Examples, very detailed and very well explained, are often centered on business scenarios. The author recommends its use as a textbook in an academic setting. … this book makes for an excellent reference manual … this book offers an overall description of how business forecasts are made, helping them understand what they pay their data scientists for.” (Andrea Paramithiotti, Computing Reviews, March 19, 2024)

Author Information

Walter R. Paczkowski earned his Ph.D. in Economics at Texas A&M University and has worked at AT&T's Analytical Support Center, Market Analysis and Forecasting Division, and Business Research Division.  He was also a Member of the Technical Staff at AT&T Bell Labs before founding Data Analytics Corp., a statistical consulting and data modeling company, in 2001.  Dr. Paczkowski is a part-time lecturer in the Department of Economics and the Department of Statistics at Rutgers University.  He published six books in, what he refers to as, his Analytics Series.  His latest are Business Analytics: Data Science for Business Problems (Springer, 2021) and Modern Survey Analysis: Using Python for Deeper Insights (Springer, 2022).

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