Books

These are some books written by members of the meetup.

Text Mining with R

Julia Silge & David Robinson

Service-Oriented Design with Ruby and Rails

Paul Dix

Doing Data Science

Cathy O'Neil & Rachel Schutt

A Computational Approach to Statistical Learning

Bryan Lewis and Michael Kane

Data Science at the Command Line

Jeroen Janssens

Testing R Code

Richard Cotton

Data Science on AWS

Chris Fregly & Antje Barth

Python for Data Analysis

Wes McKinney

A Quantitative Tour of the Social Sciences

Andrew Gelman & Jeronimo Cortina

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

Hadley Wichkam

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

Tony Fischetti

Bayesian Data Analysis

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

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

JD Long

Analyzing the Analyzers

Harlan Harris, Sean Patrick Murphy & Marck Vaisman

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

Hadley Wickham

Machine Learning for Hackers

Drew Conway & John Myles White

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

Hadley Wickham

Bad Data

Q. Ethan McCallum

Getting Started with D3

Mike Dewar

Deep Learning with R

JJ Allaire

Advanced R: Second Edition

Hadley Wickham

Red State, Blue State, Rich State, Poor State

Andrew Gelman

Forecasting: principles and practice

Rob Hyndman

Pandas for Everyone: Python Data Analysis

Daniel Y. Chen

R for Everyone

Jared P. Lander

Learning R

Richard Cotton

Teaching Statistics: A Bag of Tricks

Andrew Gelman & Deborah Nolan

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

Richard Cotton

Applied Predictive Modeling

Max Kuhn & Kjell Johnson

Introduction to Empirical Bayes: Examples from Baseball Statistics

David Robinson

Bandit Algorithms for Website Optimization

John Myles White

Data Analysis Using Regression and Multilevel/Hierarchical Models

Andrew Gelman & Jennifer Hill

Machine Learning for Email

Drew Conway & John Myles White

Parallel R

Q. Ethan McCallum

Feature Engineering and Selection: A Practical Approach for Predictive Models

Max Kuhn