Books and other sources I recommend

 

Language-specific

R

Simply the best beginner-to-intermediate resource for data science with R: R for Data Science (previously “Data Science with R”)

Further exploration of R’s inner workings, appropriately titled: Advanced R

I also recommend doing a full RTFM of the data.table package, as it’s been invaluable in reading, processing, and writing tabular data in R efficiently. Unfortunately, I have found few external resources that simultaneously simply, clearly, and accurately describe its use, syntax, and benefits. (I’m currently working on producing my own.) I personally prefer data.table to the “Hadleyverse” equivalents dplyr and such, and would attribute much of Hadley Wickham’s success to just how clear his writing is (more below).

Other resources on ggplot2 and the rest contain a lot of cut-and-paste code without sufficient explanation. Ironically, I’d just head straight to the publications for the best look into how these systems work and how to use them.

 

Python

There are simply too many (easy-to-find) ways to get started with Python to list any here, and none have stood out to me particularly.

The “salamander” is the clearest and most comprehensive resource I’ve found on machine learning, scikit-learn, and TensorFlow through Python.

 

Math etc.

Confused about neural networks? Want to know what gradient descent is or how backpropagation works? Curious about blockchain? Curious about anything else in math (problem-solving, linear algebra, nonsense)? Keep hearing about “igon” (eigen) vectors/values/bases and wish you knew what they were? Do you like it when a concept explains itself to you in the form of an anthropomorphized pi symbol? If your answer to any of those questions was even vaguely affirmative, peruse 3Blue1Brown on YouTube. You’ll be happy you did.

 

General

The Black Swan: The Impact of the Highly Improbable, Nassim Taleb –– shake up your views on the limits of knowledge. I think about the Platonic Fold all the time.

The Signal and the Noise, Nate Silver–– an inside look into the world of prediction, with a few interesting stories. Nicely outlines where predictions are feasible and where they aren’t.

The Sense of Style, Steven Pinker –– improve your understanding of writing and communication.