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Managerial Analytics: An Applied Guide to Principles, Methods, Tools, and Best Practices

By Michael Watson, Derek Nelson, Peter Cacioppi

Published by Pearson FT Press

Published Date: Dec 20, 2013


The field of analytics is rapidly evolving, making it difficult for professionals and students to keep up the most current and effective applications.  Managerial Analytics will help readers sort through all these new options and identify the appropriate solution. In this reference, authors Watson, Nelson and Cacioppi accurately define and identify the components of analytics and big data, giving readers the knowledge needed to effectively assess new aspects and applications. Building on this foundation, they review tools and solutions, identify the offerings best aligned to one’s requirements, and show how to tailor analytics applications to an organization’s specific needs.  Drawing on extensive experience implementing, planning, and researching advanced analytics for business, the authors clearly explain all this, and more:  

  • What analytics is and isn’t: great examples of successful usage – and other examples where the term is being degraded into meaninglessness
  • The difference between using analytics and “competing on analytics”
  • How to get started with big data, by analyzing the most relevant data
  • Components of analytics systems, from databases and Excel to BI systems and beyond
  • Anticipating and overcoming “confirmation bias” and other pitfalls
  • Understanding predictive analytics and getting the high-quality random samples necessary
  • Applying game theory, Efficient Frontier, benchmarking, and revenue management models
  • Implementing optimization at the small and large scale, and using it to make “automatic decisions”

Table of Contents

Preface     xv

Part I Overview     1
Chapter 1 What Is Managerial Analytics?     3
Chapter 2 What Is Driving the Analytics Movement?     23
Chapter 3 The Analytics Mindset     35

Part II Analytics Toolset     63
Chapter 4 Machine Learning     65
Chapter 5 Descriptive Analytics     93
Chapter 6 Predictive Analytics     139
Chapter 7 Case Study: Moneyball and Optimization     155
Chapter 8 Prescriptive Analytics (aka Optimization)     163

Part III Conclusion     199
Chapter 9 Revenue Management     201
Chapter 10 Final Tips for Implementing Analytics     211

Nontraditional Bibliography and Further Reading     215
Endnotes     221
Index     227