Introduction: Why you need to understand data analytics -- Section 1. Getting started: Keep up with your quants: an innumerate's guide to navigating big data / by Thomas H. Davenport -- A simple exercise to help you think like a data scientist: an easy way to learn the process of data analytics / by Thomas C. Redman -- Section 2. Gather the right information: Do you need all that data?: questions to ask for a focused search / by Ron Ashkenas -- How to ask your data scientists for data and analytics: factors to keep in mind to get the information you need / by Michael Li, Madina Kassengaliyeva, and Raymond Perkins -- How to design a business experiment: tips for using the scientific method / by Oliver Hauser and Michael Luca -- Know the difference between your data and your metrics: understand what you're measuring / by Jeff Bladt and Bob Filbin -- The fundamentals of A/B testing: how it works and mistakes to avoid / by Amy Gallo -- Can your data be trusted?: gauge whether your data is safe to use / by Thomas C. Redman -- Section 3. Analyze the data: A predictive analytics primer: look to the future by looking at the past / by Thomas H. Davenport -- Understanding regression analysis: evaluate the relationship between variables / by Amy Gallo -- When to act on a correlation, and when not to: assess your confidence in your findings and the risk of being wrong / by David Ritter -- Can machine learning solve your business problem?: steps to take before investing in AI / by Anastassia Fedyk -- A refresher on statistical significance: check if your results are real or just luck / by Amy Gallo -- Linear thinking in a nonlinear world: a common mistake that leads to errors in judgment / by Bart de Langhe, Stefano Puntoni, and Richard Larrick -- Pitfalls of data-driven decisions: the cognitive traps to avoid / by Megan MacGarvie and Kristina McElheran -- Don't let your analytics cheat the truth: always ask for the outliers / by Michael Schrage -- Section 4. Communicate your findings: Data is worthless if you don't communicate it: tell people what it means / by Thomas H. Davenport -- When data visualization works, and when it doesn't: not all data is worth the effort / by Jim Stikeleather -- How to make charts that pop and persuade: questions to help give your numbers meaning / by Nancy Duarte -- Why it's so hard for us to communicate uncertainty: illustrating - and understanding - the likelihood of events: an interview with Scott Berinato / by Nicole Torres -- Responding to someone who angrily challenges your data: ensure the data is thorough, then make them an ally / by Jon M. Jachimowicz -- Decisions don't start with data: influence others through story and emotion / by Nick Morgan
