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

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

  • Ft Pr(2013/12発売)
  • ただいまウェブストアではご注文を受け付けておりません。 ⇒古書を探す
  • 製本 Hardcover:ハードカバー版/ページ数 236 p.
  • 言語 ENG
  • 商品コード 9780133407426
  • DDC分類 658.403

Full Description


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"

Contents

Preface xv Part I Overview 1Chapter 1 What Is Managerial Analytics? 3Chapter 2 What Is Driving the Analytics Movement? 23Chapter 3 The Analytics Mindset 35 Part II Analytics Toolset 63Chapter 4 Machine Learning 65Chapter 5 Descriptive Analytics 93Chapter 6 Predictive Analytics 139Chapter 7 Case Study: Moneyball and Optimization 155Chapter 8 Prescriptive Analytics (aka Optimization) 163 Part III Conclusion 199Chapter 9 Revenue Management 201Chapter 10 Final Tips for Implementing Analytics 211 Nontraditional Bibliography and Further Reading 215Endnotes 221Index 227

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