- Item type
- Book
- Language
- English
- Publication year
- 2018
- ISBN
- 978-1-63369-428-6
"Today's business environment brings with it an onslaught of data, but leaving the analysis to others in your company just won't cut it. Now more than ever, managers must know how to tease insight from data--to understand where it comes from, make sense of the numbers, and use those findings to inform your toughest decisions. How do you get started? Whether you're working with data experts or running your own tests, the "HBR Guide to Data Analytics Basics for Managers" provides practical tips and advice to help you make better decisions using data. Through its three-step process, this essential guide will show you how to get the information you need, study the data, and communicate your findings to others. You'll learn to: Identify the metrics you need to measure; Formulate hypotheses and test against them; Ask the right questions of your data--and your data experts; Understand statistical terms and concepts; Create effective charts and visualizations; Avoid common mistakes." - from publisher.
Introduction: Why you need to understand data analytics --- SECTION ONE: GETTING STARTED --- Chapter 1: Keep up with your quants --- Chapter 2: A simple exercise to help you think like a data scientist --- SECTION TWO: GATHER THE RIGHT INFORMATION --- Chapter 3: Do you need all that data? --- Chapter 4: How to ask your data scientists for data and analytics --- Chapter 5: How to design a business experiment --- Chapter 6: Know the difference between your data and your metrics --- Chapter 7: The fundamentals of A/B testing --- Chapter 8: Can your data be trusted? --- SECTION THREE: ANALYZE THE DATA --- Chapter 9: A predictive analytics primer --- Chapter 10: Understanding regression analysis --- Chapter 11: When to act on a correlation, and when not to --- Chapter 12: Can machine learning solve your business problem? --- Chapter 13: A refresher on statistical significance --- Chapter 14: Linear thinking in a nonlinear world --- Chapter 15: Pitfalls of data-driven decisions --- Chapter 16: Don't let your analytics cheat the truth --- SECTION FOUR: COMMUNICATE YOUR FINDINGS --- Chapter 17: Data is worthless if you don't communicate it --- Chapter 18: When data visualization works - and when it doesn't --- Chapter 19: How to make charts that pop and persuade --- Chapter 20: Why it's so hard for us to communicate uncertainty --- Chapter 21: Responding to someone who challenges your data --- Chapter 22: Decisions don't start with data --- Appendix: Data scientist: the sexiest job of the 21st century..
