Although I might have had a knack for math growing up, prior to starting my clinical training (i.e., residency), I had no formal statistical or analytical training, but was able to learn from a number of available textbooks and online references. I've listed some of the most useful ones below.
(Note: If you want resources for machine learning, see this updated post.)
Introductory Material
This textbook was the first book I ever read on statistics. Excellent introduction to statistics and probability
This book was by far the best high-level explanation of both statistics and epidemiology. I still use references from it for teaching (particularly the explanation of confounding).
The lectures on linear algebra, calculus, and (of course) statistics, were an excellent supplement, and necessary to understand multivariate regression.
Bread and butter stats
A challenging book as an introduction, with some advanced mathematical explanations, but nonetheless an excellent overview of regression approaches.
A very practical explanatory approach to logistic regression by some of the pioneers of logistic regression (i.e., they have tests named after them).
As with the logistic regression textbook, this one also offers a very practical explanatory approach to survival analysis.
More advanced topics
This textbook provided a very comprehensive understanding of longitudinal analysis. The lecture slides from the website are a nice compliment, as was code from the UCLA statistics website, which is from a different textbook, but useful nonetheless.
This textbook is a very comprehensive, and somewhat technical, description of the various methods of time series analysis. Would also suggest this excellent online resource as a supplement.
Statistical Tools
Most of the Statistical code we'll be posting will be in the stats package R, which can be obtained here. R is open source, and very accessible for writing scripts and sharing code, which makes it ideal for an open/sharing platform.
Stata is also a very excellent, and user-friendly statistical package that I use for much of my 'bread-and-butter' statistics work. It has drop-down menus, wonderful supportive documentation, and is a great way to get started with statistical analysis for those who don't have experience with coding. This textbook was useful for me getting started, but there are others available through the Stata website.
Computational Resources
This textbook was a tremendous resource for someone who had never done anything computationally prior. You will be amazed how useful some of the tricks (especially the regular expressions stuff), even if you don't work in a computational biology area.
The MIT OpenCourseWare selection of online courses is a tremendous resource. I personally took the Introduction to Computer Science course, with the accompanying textbook, which was well-taught and easy to follow.
I used several textbooks from the Big Nerd Ranch, including programming in Swift and iOS design. An excellent resource, easy to follow, and you get to design some cool apps along the way.
Compiled by Michael Rosenberg, April 2017.