Helpful Resources and Links

These are Ryan’s favorite bookmarks. They cover everything from programming 101 to deep learning. If you have have a suggestion, please contact Ryan.

Data Science Resources

An Introduction to Statistical Learning

The Elements of Statistical Learning

Foundations of Machine Learning

R Resources

R cookbook

R for Data Science

Mastering Shiny

Tidy Modeling with R. My absolute favorite way to develop models.

Python Resources

A Whirlwind Tour of Python, by Jake VanderPlas A solid introduction to Python, assuming you are already familiar with another programming language.

Python Data Science Handbook

Pythex | In-browser regex tester

Data Visualization Resources

From Data to Viz. A fantastic guide to all types of plots by Yan Holtz and Conor Healy. Also check out their Dataviz Inspiration page.

BBC Visual and Data Journalism cookbook for R graphics

Cedric Scherer’s Data Visualization & Information Design

Data Engineering Resources

The Ultimate Guide to Deploying a Shiny App on AWS, by Charles Bordet. I personally used the guide to launch my own EC2 servers.

Oh Shit, Git!?! You’ve messed up. Bad. This resource might just save you.

Intro to SQL: Querying and managing data

Math Resources

Engineering Biostatistics: An Introduction Using MATLAB and WinBUGS by Brani Vidakovic. Excellent resources for Bayesian statistics, MATLAB and WinBugs.

Wolframe|Alpha. Powerful in-browser computational aid.

Symbolab. Another powerful in-browser computational aid. Usually provides steps to solve problems at no extra cost.

Octave. Open source software to run those pesky .m scripts (Free alternative to MatLab)

OpenBUGS. Free Bayesian software. I prefer to use R or Python.

Calculus Made Easy by S. P. Thompson

Mathematics for Machine Learning

The Matrix Cookbook

Introduction to Applied Linear Algebra