These are some books written by members of the meetup.

Learning R: A Step-by-Step Function Guide to Data Analysis

Richard Cotton

A Computational Approach to Statistical Learning

Bryan Lewis and Michael Kane

Introduction to Empirical Bayes: Examples from Baseball Statistics

David Robinson

Red State, Blue State, Rich State, Poor State

Andrew Gelman

Text Mining with R

Julia Silge & David Robinson

Parallel R

Q. Ethan McCallum

Testing R Code

Richard Cotton

Machine Learning for Email

Drew Conway & John Myles White

Analyzing the Analyzers

Harlan Harris, Sean Patrick Murphy & Marck Vaisman

Service-Oriented Design with Ruby and Rails

Paul Dix

Applied Predictive Modeling

Max Kuhn & Kjell Johnson

Deep Learning with R

JJ Allaire

Bandit Algorithms for Website Optimization

John Myles White

R for Everyone

Jared P. Lander

Data Science on AWS

Chris Fregly & Antje Barth

Python for Data Analysis

Wes McKinney

Machine Learning for Hackers

Drew Conway & John Myles White

Learning R

Richard Cotton

R Packages: Organize, Test, Document, and Share Your Code

Hadley Wichkam

Getting Started with D3

Mike Dewar

Bad Data

Q. Ethan McCallum

Bayesian Data Analysis

Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari & Donald B. Rubin

ggplot2: Elegant Graphics for Data Analysis (Use R!)

Hadley Wickham

Forecasting: principles and practice

Rob Hyndman

Doing Data Science

Cathy O'Neil & Rachel Schutt

Pandas for Everyone: Python Data Analysis

Daniel Y. Chen

Advanced R: Second Edition

Hadley Wickham

Feature Engineering and Selection: A Practical Approach for Predictive Models

Max Kuhn

Data Analysis with R - Second Edition: A comprehensive guide to manipulating, analyzing, and visualizing data in R

Tony Fischetti

A Quantitative Tour of the Social Sciences

Andrew Gelman & Jeronimo Cortina

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

Hadley Wickham

Data Analysis Using Regression and Multilevel/Hierarchical Models

Andrew Gelman & Jennifer Hill

Teaching Statistics: A Bag of Tricks

Andrew Gelman & Deborah Nolan

Data Science at the Command Line

Jeroen Janssens

R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics

JD Long




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