Last Updated: January 4, 2017
Hi! My name is Danny Vilela. You can find out more about me, but otherwise this is my (online) bookshelf. Digital bookshelves are nice in that they aren’t bound to just books – they can also hold online courses and other resources that I tend to appreciate as I learn more about data science!
Most of these books have an online/free equivalent, but I also provide a link to the Amazon listing (if possible) if you’re like me and need a physical book sometimes. For each listing I’ll do my best to include a more in-depth review of each, along with any other resources I found useful in covering that resource.
Have a great read,
|An Introduction to Statistical Learning||Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani|
This book is pretty well-known for presenting important modeling and prediction techniques. Topics like linear regression, classification, resampling methods, tree-based methods, clustering, and more are covered in such an approachable way that even I’m able to understand what’s going on :)
|R for Data Science||Garrett Grolemund, Hadley Wickham|
Not on my physical bookshelf *yet* but R for Data Science is a great, approachable read for an introduction to data science using R. The book covers a wide variety of data science tasks that have natural approaches using R. It covers a great deal of practical data science, including:
The print book is sure to be pretty dang good, but the online book is constantly updated through pull requests, which beats (a) learning something that’s incorrect from a print book and (b) checking the errata for your particular mistake.
|The Signal and the Noise||Nate Silver|
The Signal and the Noise is a fantastic inter-disciplinary book that finds statistics in science, economics, and prediction. It’s filled with incredibly interesting analyses, offers insight into a leading statistician’s intuition, and is one of the most approachable reads on Bayesian statistics I’ve yet found.
This book isn’t heavy on statistics, and is actually a great casual read for anyone quantitatively-minded or interested in prediction. I highly recommend a physical copy to litter with notes — it’s worth it.