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

Book 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”

  • Table of Contents

    1. About This eBook
    2. Title Page
    3. Copyright Page
    4. Praise for Managerial Analytics
    5. Dedication Page
    6. Contents
    7. Acknowledgments
    8. About the Authors
    9. Preface
    10. Part I: Overview
      1. 1. What Is Managerial Analytics?
        1. Confusion About the Meaning of Analytics
        2. What Is Analytics?
        3. So, What’s New?
        4. What Is the Best Type of Analytics?
        5. What Is Managerial Analytics?
      2. 2. What Is Driving the Analytics Movement?
        1. Data Is the Fuel for Analytics
        2. Testing Helps Ignite Analytics
      3. 3. The Analytics Mindset
        1. Managerial Innumeracy
        2. Analytics Is a Mindset
        3. Thinking Clearly About Data
        4. The Rise of the Data Scientist
    11. Part II: Analytics Toolset
      1. 4. Machine Learning
        1. Introduction to Machine Learning
        2. Supervised Machine Learning Algorithms
        3. Unsupervised Machine Learning
        4. A Note About Over- and Under-Fitting Your Models
        5. Other Machine Learning Algorithms and Summary
      2. 5. Descriptive Analytics
        1. Descriptive Analytics Through Databases
        2. Descriptive Analytics Through Visualization
        3. Descriptive Analytics Through Descriptive Statistics
        4. Descriptive Analytics Through Machine Learning
      3. 6. Predictive Analytics
        1. Forecasting with Regression
        2. Machine Learning and Ensemble Models
        3. A/B Testing
        4. Simulation
      4. 7. Case Study: Moneyball and Optimization
      5. 8. Prescriptive Analytics (aka Optimization)
        1. What Is Optimization?
        2. Optimization = Targets, Limits, Choices + Data
        3. TLC+D in Action: Everyone Loves Pizza
        4. Types of Optimization Algorithms
    12. Part III: Conclusion
      1. 9. Revenue Management
      2. 10. Final Tips for Implementing Analytics
        1. It Is the Archer, Not the Arrow
        2. Wrapping Up
    13. Nontraditional Bibliography and Further Reading
    14. Endnotes
      1. Chapter 1
      2. Chapter 2
      3. Chapter 3
      4. Chapter 4
      5. Chapter 5
      6. Chapter 6
      7. Chapter 8
      8. Chapter 9
    15. Index