{"product_id":"hbr-guide-to-data-analytics-basics-for-m","title":"HBR Guide to Data Analytics Basics for Managers","description":"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","brand":"Harvard Business Review Press","offers":[{"title":"Default Title","offer_id":47521782235361,"sku":null,"price":525.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0779\/9033\/0593\/files\/18190353482769.jpg?v=1764169853","url":"https:\/\/perfectbookhouse.com\/products\/hbr-guide-to-data-analytics-basics-for-m","provider":"PERFECT BOOK HOUSE","version":"1.0","type":"link"}