Predictive and Simulation Analytics

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.

Emphasizes predictive and simulation analytics in complex systems Presents methods for different system perspectives: operational, tactical, and strategic Demonstrates concepts with programming examples, provided in Jupyter notebooks

Autorentext

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).

Inhalt
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.