Managerial Analytics: An Applied Guide to Principles, Methods, Tools, and Best Practices [NOOK Book]

Overview

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 ...

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

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Overview

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”
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Product Details

  • ISBN-13: 9780133408645
  • Publisher: Pearson Education
  • Publication date: 12/10/2013
  • Series: FT Press Analytics
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • Pages: 256
  • File size: 4 MB

Meet the Author

Michael Watson is currently a partner at Opex Analytics and an Adjunct Professor at Northwestern University. At Opex Analytics he helps bring new analytics solutions to companies. Prior to Opex Analytics, he was a manager at IBM in the ILOG supply chain and optimization group. At Northwestern, he teaches a program on operations management and managerial analytics in the McCormick School of Engineering’s Masters in Engineering Management (MEM). He teaches optimization in Northwestern’s Master of Science in Analytics program. He holds an M.S. and Ph.D. from Northwestern University in Industrial Engineering and Management Sciences.

Derek Nelson is currently a senior principal at OPS Rules and an Adjunct Professor at Northwestern University. At OPS Rules, Derek leverages analytics to help companies improve operational performance. Prior to OPS Rules, Derek held consulting, product management, and technical sales roles in optimization and supply chain software for LogicTools, ILOG, and IBM. At Northwestern, Derek has taught service operations management to undergraduates in the Industrial Engineering and Management Sciences department and will soon be teaching in the Master in Engineering Management (MEM) program. Derek holds an M.S. in Operations Research from Cornell University.

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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

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