= Intro Stats =
* [http://web.bryant.edu/~bblais/statistical-inference-for-everyone-sie.html Statistical inference for everyone] - a free online textbook (and python module) for learning basic stats
* [http://www-bcf.usc.edu/~gareth/ISL/ An Introduction to Statistical Learning with Applications in R] - lets call this 'Intermediate'
* [https://a-little-book-of-r-for-bioinformatics.readthedocs.org/en/latest/ A little book of R for bioinformatics] - From scratch, get-up-and-go guide. Good for beginners.
* PCA
 * [http://setosa.io/ev/principal-component-analysis/ How to think about PCA]
 * [http://joelgrus.com/2013/06/24/t-shirts-feminism-parenting-and-data-science-part-2-eigenshirts/ Creative application of PCA to t-shirts], and [http://blog.thehackerati.com/post/126701202241/eigenstyle dresses]. Really cool.
 * [http://deeplearning4j.org/eigenvector A Beginner’s Guide to Eigenvectors, PCA, Covariance and Entropy]
= Bayesian =
* [http://technology.stitchfix.com/blog/2015/02/12/may-bayes-theorem-be-with-you/ Bayesian vs frequentist thinking.]
* [http://sciencehouse.wordpress.com/2010/06/23/mcmc-and-fitting-models-to-data/ Thorough but gentle introduction to bayesian inference and MCMC]
* [http://www.sumsar.net/blog/2014/10/tiny-data-and-the-socks-of-karl-broman/ Creative application of ABC. How many socks does Karl Broman have?]
* [http://ipython-books.github.io/featured-07/ Introduction to statistical data analysis in Python – frequentist and Bayesian methods]

= GWAS =
* [http://sciencehouse.wordpress.com/2012/04/09/heritability-and-gwas-3/ Great intro to history, methods, and application of GWAS]