Thread by @ChelseaParlett

📚 A Few of My Favorite Data Science Related Textbooks

(I use the term textbook loosely)

Here are some (non-exhaustive) of my favorites: https://t.co/BCgfQadCmN

📕 Elements of Statistical Learning

This book was the BIBLE in Grad School. It’s incredibly in depth and dense, but not so much that you can’t get through it. It’s comprehensive, well written, and is my go to reference to understand a ML algo more deeply😍

📗Introduction to Statistical Learning with Applications in R

A gentler cousin of ESL, ISLR (and now ISLP!) is an great intro to ML algos. This book can be appreciated by undergrads, grads, and industry workers alike. The code examples are incredibly useful, and text is clear

(I dont currently assign a required textbook in my course, but if I ever do, it’s ISLP 100%)

📘Deep Learning

Not for the faint of 🫀! Like ESL, DL is a DENSE BOOK. There are chapters I’ve read half a dozen times and I’m still learning more each time. This book saved my 🍑 in grad school over and over. And I go to it often when prepping my deep learning course.

📙Pattern Recognition and Machine Learning

This book DIGS INTO the math, and yet remains clear and has a great flow. ML Math can be intimidating but this book takes you by the hand and says “look, you have to learn this but I’m going to help”

📕Generative Deep Learning

Unlike the previous entries, this book isn’t a comprehensive reference book, it’s a gentle and clear intro to generative deep learning models. I read it to recommend to my undergrads and it’s perfect for them. It’s clear and has great figures.

📗Tidy Modeling With R

Julia Silge and @topepos have DONE IT AGAIN. I had the honor of tech reviewing this book and it is gold cover to cover. If you’re doing ML in R, you must read this book. They explain things so well, and their graph game is unmatched. Just, gorgeous content

@topepos 📘Statistical Rethinking

My emotional support textbook 🥹 @rlmcelreath has a way of writing that makes you EXCITED about what he’s saying. From basic causality to Bayesian stuff, he makes you go “i can’t wait to use this tool”. Bayesians and non-Bayesians alike will enjoy.

@topepos @rlmcelreath 📙Regression and Other Stories

This book is such a joy to read. You want to deeply understand regression? This is the book. I often go to this when consulting because it helps me explain models to my clients😍 these are the ideas and models that social scientists NEED to know

@topepos @rlmcelreath 📕Regression Modeling Strategies

@f2harrell is the G.O.A.T. This book is like an encyclopedia for all the regression models you’re dying to use in your work. From ordinal models, to survival analysis…this book has it all. And endless case studies to see them in action.

@topepos @rlmcelreath @f2harrell 📕How to Ace Calculus: the Streetwise Guide

This book saved me in college calculs *mumbles* years ago. It’s silly, it’s clear, it makes learning calculus few FUN. This type of book really molded my scicomm style🥹

@topepos @rlmcelreath @f2harrell 📗Bayesian Data Analysis

This book is Jam packed with Bayesian goodness. It’s not the first book I’d give to a newbie, but it’s the book I think every practitioner should have on their desk if they’re going to use Bayesian Models. It has everything you want to know and 10% more

@topepos @rlmcelreath @f2harrell 📘Think Like a Progammer

If you’re learning C++ but are already familiar with a higher level language like python or R, this will help you wrap your brain around some lower level concepts (wtf is a pointer and how does it relate to an asterisk?!?)