Books

These are some books written by members of the meetup.

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

Richard Cotton

Analyzing the Analyzers

Harlan Harris, Sean Patrick Murphy & Marck Vaisman

Text Mining with R

Julia Silge & David Robinson

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

Tony Fischetti

Machine Learning for Email

Drew Conway & John Myles White

Deep Learning with R

JJ Allaire

Data Analysis Using Regression and Multilevel/Hierarchical Models

Andrew Gelman & Jennifer Hill

Forecasting: principles and practice

Rob Hyndman

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

Hadley Wichkam

Bandit Algorithms for Website Optimization

John Myles White

Bayesian Data Analysis

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

Parallel R

Q. Ethan McCallum

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

JD Long

Getting Started with D3

Mike Dewar

Data Science at the Command Line

Jeroen Janssens

Red State, Blue State, Rich State, Poor State

Andrew Gelman

Data Science on AWS

Chris Fregly & Antje Barth

Introduction to Empirical Bayes: Examples from Baseball Statistics

David Robinson

Testing R Code

Richard Cotton

Advanced R: Second Edition

Hadley Wickham

A Computational Approach to Statistical Learning

Bryan Lewis and Michael Kane

Service-Oriented Design with Ruby and Rails

Paul Dix

Applied Predictive Modeling

Max Kuhn & Kjell Johnson

Python for Data Analysis

Wes McKinney

R for Everyone

Jared P. Lander

Doing Data Science

Cathy O'Neil & Rachel Schutt

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

Hadley Wickham

A Quantitative Tour of the Social Sciences

Andrew Gelman & Jeronimo Cortina

Teaching Statistics: A Bag of Tricks

Andrew Gelman & Deborah Nolan

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

Hadley Wickham

Machine Learning for Hackers

Drew Conway & John Myles White

Bad Data

Q. Ethan McCallum

Learning R

Richard Cotton

Feature Engineering and Selection: A Practical Approach for Predictive Models

Max Kuhn

Pandas for Everyone: Python Data Analysis

Daniel Y. Chen