Cover image for HBR Guide to Data Analytics Basics for Managers (HBR Guide Series)

HBR Guide to Data Analytics Basics for Managers (HBR Guide Series)

Author Harvard Business Review
Release Date: 2018/03/01
ISBN: 9781633694293
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Book Description

Don't let a fear of numbers hold you back.

Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. How do you get started?

Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others.

You'll learn how to:

  • Identify the metrics you need to measure
  • Run experiments and A/B tests
  • Ask the right questions of your data experts
  • Understand statistical terms and concepts
  • Create effective charts and visualizations
  • Avoid common mistakes

Table of Contents

  1. Cover
  2. Harvard Business Review Guides
  3. Title Page
  4. HBR Press Quantity Sales Discounts
  5. Copyright
  6. What You’ll Learn
  7. Contents
  8. Introduction
  9. Section One. Getting Started
    1. 1. Keep Up with Your Quants
    2. 2. A Simple Exercise to Help You Think Like a Data Scientist
  10. Section Two. Gather the Right Information
    1. 3. Do You Need All That Data?
    2. 4. How to Ask Your Data Scientists for Data and Analytics
    3. 5. How to Design a Business Experiment
    4. 6. Know the Difference Between Your Data and Your Metrics
    5. 7. The Fundamentals of A/B Testing
    6. 8. Can Your Data Be Trusted?
  11. Section Three. Analyze the Data
    1. 9. A Predictive Analytics Primer
    2. 10. Understanding Regression Analysis
    3. 11. When to Act On a Correlation, and When Not To
    4. 12. Can Machine Learning Solve Your Business Problem?
    5. 13. A Refresher on Statistical Significance
    6. 14. Linear Thinking in a Nonlinear World
    7. 15. Pitfalls of Data-Driven Decisions
    8. 16. Don’t Let Your Analytics Cheat the Truth
  12. Section Four. Communicate Your Findings
    1. 17. Data Is Worthless If You Don’t Communicate It
    2. 18. When Data Visualization Works—and When It Doesn’t
    3. 19. How to Make Charts That Pop and Persuade
    4. 20. Why It’s So Hard for Us to Communicate Uncertainty
    5. 21. Responding to Someone Who Challenges Your Data
    6. 22. Decisions Don’t Start with Data
  13. Appendix: Data Scientist: The Sexiest Job of the 21st Century
  14. Index
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