The Top Ten Most Popular Leanpub Books In 2015

by Len Epp

published Dec 16, 2015

Leanpub books are sold using a variable pricing sales model. This means that for each book, the author or publisher sets both a minimum and suggested price for the book, and the reader chooses what to pay.

This means that measuring a book’s success in the Leanpub marketplace is in some ways different from the conventional bestseller lists you see. For this reason, in the Leanpub bookstore we like to show two types of lists for the top-performing books: those that have earned the most sales revenue, or “Bestsellers”, and those that have earned the most readers, or “Popular Books”.

This distinction allows us to clearly distinguish success in terms of revenue from success in terms of the number of readers reached.

We’ve already posted our list of the top ten bestselling Leanpub books in 2015. Below is a list of the most popular books on Leanpub in 2015. Some of them are naturally enough also on the top ten bestsellers list, but some aren’t.

Perhaps the most interesting thing to note, for people interested in the ebook publishing market, is the fact that the top two books in both lists have free minimum prices.

There are even more data science books on this “Popular Books” list than there are on the “Bestsellers” list. That’s partly because so many of the data science books have a minimum price of free, which means the connection between number of readers and sales revenue is less direct than in other cases, where the minimum price of a book is not free.

At the same time, the dominance of this category of book on Leanpub is also explained by the explosive popularity of data science as a discipline. Check out the Leanpub Podcast interviews with Roger Peng, Jeff Leek and Brian Caffo if you’re interested in hearing experts talk about what data science is and why the discipline is growing so rapidly. You can also find full transcriptions of the podcasts on our blog.

If you’re interested in hearing about Malcolm Maclean’s journey onto our popular books list with https://leanpub.com/D3-Tips-and-Tricks: Interactive Data Visualization in a Web Browser, check out our interview with him here.

Thanks again to all our authors and the growing community of Leanpub readers for making 2015 such a great year!

#10. Data Analysis for the Life Sciences by Rafael A Irizarry and Michael I Love

Data Analysis for the Life Sciences by Rafael A Irizarry and Michael I Love Title: Data Analysis for the Life Sciences
Authors: Rafael A Irizarry and Michael I Love
Description: Data analysis is now part of practically every research project in the life sciences. In this book we use data and computer code to teach the necessary statistical concepts and programming skills to become a data analyst. Instead of showing theory first and then applying it to toy examples, we start with actual applications and describe the theory as it becomes necessary to solve specific challenges. The book includes links to computer code that readers can use to follow along as they program.

Tweet to Rafael and Mike at @rafalab and @mikelove

#9. PHP: The “Right” Way: Your guide to PHP best practices, coding standards, and authoritative tutorials by Phil Sturgeon and Josh Lockhart

PHP: The 'Right' Way: Your guide to PHP best practices, coding standards, and authoritative tutorials by Phil Sturgeon and Josh Lockhart Title: PHP: The “Right” Way: Your guide to PHP best practices, coding standards, and authoritative tutorials
Authors: Phil Sturgeon and Josh Lockhart
Description: There’s a lot of outdated information on the Web that leads new PHP users astray, propagating bad practices and insecure code. PHP: The Right Way is an easy-to-read, quick reference for PHP popular coding standards, links to authoritative tutorials around the Web and what the contributors consider to be best practices at the present time.

Tweet to Phil and Josh at @philsturgeon and @codeguy

8. Report Writing for Data Science in R by Roger D. Peng

Report Writing for Data Science in R by Roger D. Peng Title: Report Writing for Data Science in R
Author: Roger D. Peng
Description: This book teaches the fundamental concepts and tools behind reporting modern data analyses in a reproducible manner. As data analyses become increasingly complex, the need for clear and reproducible report writing is greater than ever. The material for this book was developed as part of the industry-leading Johns Hopkins Data Science Specialization.

Tweet to Roger at @rdpeng

7. D3 Tips and Tricks: Interactive Data Visualization in a Web Browser by Malcolm Maclean

D3 Tips and Tricks: Interactive Data Visualization in a Web Browser by Malcolm Maclean Title: D3 Tips and Tricks: Interactive Data Visualization in a Web Browser
Author: Malcolm Maclean
Description: Over 600 pages of tips and tricks for using d3.js, one of the leading data visualization tools for the web. It’s aimed at getting you started and moving you forward. Includes over 50 downloadable code examples. You can download for FREE or donate to encourage further development if you wish :-).

Tweet to Malcolm at @d3noob

6. Regression Models for Data Science in R: A companion book for the Coursera Regression Models class by Brian Caffo

Regression Models for Data Science in R: A companion book for the Coursera Regression Models class by Brian Caffo Title: Regression Models for Data Science in R: A companion book for the Coursera Regression Models class
Author: Brian Caffo
Description: This book gives a brief, but rigorous, treatment of regression models intended for practicing Data Scientists.

Tweet to Brian at @bcaffo

5. Statistical inference for data science: A companion to the Coursera Statistical Inference Course by Brian Caffo

Statistical inference for data science: A companion to the Coursera Statistical Inference Course by Brian Caffo Title: Statistical inference for data science: A companion to the Coursera Statistical Inference Course
Author: Brian Caffo
Description: This book gives a brief, but rigorous, treatment of statistical inference intended for practicing Data Scientists.

Tweet to Brian at @bcaffo

4. Exploratory Data Analysis with R by Roger D. Peng

Exploratory Data Analysis with R by Roger D. Peng Title: Exploratory Data Analysis with R
Author: Roger D. Peng
Description: This book teaches you to use R to effectively visualize and explore complex datasets. Exploratory data analysis is a key part of the data science process because it allows you to sharpen your question and refine your modeling strategies. This book is based on the industry-leading Johns Hopkins Data Science Specialization, the most widely subscribed data science training program ever created.

Tweet to Roger at @rdpeng

#3. The Art of Data Science: A Guide for Anyone Who Works with Data by Roger D. Peng and Elizabeth Matsui

The Art of Data Science: A Guide for Anyone Who Works with Data and Elizabeth Matsui Title: The Art of Data Science: A Guide for Anyone Who Works with Data
Authors: Roger D. Peng and Elizabeth Matsui
Description: This book describes the process of analyzing data in simple and general terms. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

Tweet to Roger and Elizabeth at @rdpeng and @eliza68

#2. The Elements of Data Analytic Style: A guide for people who want to analyze data by Jeff Leek

The Elements of Data Analytic Style: A guide for people who want to analyze data by Jeff Leek Title: The Elements of Data Analytic Style: A guide for people who want to analyze data.
Author: Jeff Leek
Description: A guide for people who want to analyze data.

Tweet to Jeff at @jtleek

1. R Programming for Data Science by Roger D. Peng

R Programming for Data Science by Roger D. Peng Title: R Programming for Data Science
Author: Roger D. Peng
Description: This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. The skills taught in this book will lay the foundation for you to begin your journey learning data science.

Tweet to Roger at @rdpeng

– Posted by Len Epp

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