Statistics Books to Read This Summer
June 27, 2019
The sunshine is calling and so is summer reading! You’ve worked hard this school year and deserve a well-earned break. This is Statistics is here to keep your statistics skills sharp, and who knows, maybe you could learn something new!
Here’s our round-up of books to put in your beach bag, take on vacation or read with the comfort of your air conditioning this summer.
By Safiya Umoja Noble
When you look something up online, you probably expect to get an unbiased answer, but that’s not necessarily true. Author Safiya Umoja Noble explains how negative biases are embedded in search engines, with a specific focus on women of color.
Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks
By Will Kurt
Publishing soon, this book looks at Bayesian statistics with a comprehensive guide that uses off-the-beaten-path examples to explain principles. “Bayesian Statistics the Fun Way” is a great departure from standard textbooks but will still give you the pay-off of real-world statistics skills.
By Judea Pearl and Dana Mackenzie
We’ve all heard “correlation is not causation.” Computer scientist and philosopher Judea Peal and mathematician Dana Mackenzie explore the history of casual inference and how we can quickly decipher basic causality, but when it comes to more pressing questions, it gets increasingly complicated.
By Carl Shan, Henry Wang, William Chen and Max Song
To all you future data scientists out there, look no further than “The Data Science Handbook” for all the advice you need from 25 current data scientists. This book will give you an inside look into the careers of some of the best in the field and the advice they have for the next generation.
By Giorgia Lupi and Stefanie Posavec
Follow the lives of Giorgia Lupi and Stefanie Posavec, two information designers who highlight their daily lives through a set of postcards that contain just as much data as they do emotion. This compilation of mail art and data visualizations just might inspire you to map your own life!
By Jordan Ellenberg
Deemed the “freakonomics of math,” this book empowers all readers to use math to their advantage. “How Not to Be Wrong” will challenge you to think outside the box in terms of what math means and what it can do. It also looks into how math has been used throughout history.
By RJ Andrews
Data can tell stories, but how can we transform this data into compelling visuals that have real impact? Award-winning data storyteller RJ Andrews gives practical advice on developing visuals, along with the lessons we can take from print advertising, engineering, museum curation and more.
By David McCandless
If you can’t get enough of stunning infographics and data visualizations, then you should definitely flip through “Knowledge is Beautiful.” The book includes a plethora of graphics that span many fields and are sure to give you inspiration for your next data visualization.
By Scott E. Page
There’s data everywhere, but how can you make it work for you? Social scientist Scott E. Page can help you with this through his “many-model” paradigm, where he uses multiple models to make better choices and predictions.
By Charles Wheelan
This New York Times best-seller will give you a basic overview of statistics principles presented in a useful and relatable way. The author uses real-world examples that not only will help you fine-tune your statistics skills but may inspire you to learn more about how statistics is used in a new field.
By Nate Silver
We love FiveThirtyEight, so naturally, we recommend reading founder Nate Silver’s “The Signal and the Noise.” Silver delves into a variety of industries and examines how they make predictions. Unsurprisingly, the people who make the best predictions are the ones who use data and probability.
By Cathy O’Neil
This fascinating read by data scientist Cathy O’Neil delves into how algorithms play into our everyday lives, but more importantly, how they reinforce biases. For example, algorithms can be discriminatory when sorting resumes, determining health insurance coverage, setting parole or denying loans. With big data comes big responsibility.
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