Books

These are some books written by members of the meetup.

Service-Oriented Design with Ruby and Rails

Paul Dix

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

Richard Cotton

Feature Engineering and Selection: A Practical Approach for Predictive Models

Max Kuhn

Doing Data Science

Cathy O'Neil & Rachel Schutt

Forecasting: principles and practice

Rob Hyndman

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

JD Long

Bad Data

Q. Ethan McCallum

Advanced R: Second Edition

Hadley Wickham

A Quantitative Tour of the Social Sciences

Andrew Gelman & Jeronimo Cortina

Analyzing the Analyzers

Harlan Harris, Sean Patrick Murphy & Marck Vaisman

Getting Started with D3

Mike Dewar

Red State, Blue State, Rich State, Poor State

Andrew Gelman

Introduction to Empirical Bayes: Examples from Baseball Statistics

David Robinson

Machine Learning for Hackers

Drew Conway & John Myles White

Bayesian Data Analysis

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

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

Tony Fischetti

Text Mining with R

Julia Silge & David Robinson

Data Science at the Command Line

Jeroen Janssens

Teaching Statistics: A Bag of Tricks

Andrew Gelman & Deborah Nolan

Data Analysis Using Regression and Multilevel/Hierarchical Models

Andrew Gelman & Jennifer Hill

Data Science on AWS

Chris Fregly & Antje Barth

Applied Predictive Modeling

Max Kuhn & Kjell Johnson

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

Hadley Wickham

Learning R

Richard Cotton

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

Hadley Wichkam

A Computational Approach to Statistical Learning

Bryan Lewis and Michael Kane

Bandit Algorithms for Website Optimization

John Myles White

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

Hadley Wickham

Deep Learning with R

JJ Allaire

Python for Data Analysis

Wes McKinney

Testing R Code

Richard Cotton

Pandas for Everyone: Python Data Analysis

Daniel Y. Chen

R for Everyone

Jared P. Lander

Parallel R

Q. Ethan McCallum

Machine Learning for Email

Drew Conway & John Myles White