What is data science and what you need to know to be a data scientist?

Joel Grus, in his book writes “There’s a joke that says a data scientist is someone who knows more statistics than a computer scientist and more computer science than a statistician. (I didn’t say it was a good joke.)”

**A set of good books to get started:**

Data Science from Scratch: First Principles with Python by Joel Grus

Doing Data Science: Straight Talk from the Frontline by Cathy O’Neil and Rachel Schutt

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney

Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Trevor Hastie, Robert Tibshirani, Jerome Friedman

Deep Learning with Python by Francois Chollet

**Newsletters:**

Exponential View by Azeem Azhar

Import AI by jack@jack-clark.net @jackclarksf

Inside AI by Rob May

Machine Learning Yearning by Andrew Ng

**Online courses**

Practical Deep Learning For Code – fast.ai Free

deeplearning.ai by Andrew Ng on Coursera. You have to pay for this course. But you can access the videos by selecting Enrol and then Audit. Coursera has other Machine learning courses for free.

Deep Learning Nanodegree by Sebastian Thrun and others on Udacity. You have to pay for this course. In Udacity you can find also free courses, such as the Deep Learning course by Google.

Berkeley Deep Reinforcement Learning 2017

Stanford CS231n: Convolutional Neural Networks for Visual Recognition

Foundations of Data Science: Computational Thinking with Python, Berkeley by Ani Adhikari, John DeNero, David Wagner

Google Machine Learning and AI courses

Depth First Learning DFL is a compendium of curricula to help you deeply understand Machine Learning.

Data Science and Robots, an online data science course by Brandon Rohrer (Data Scientist at Facebook)

Hardware Accelerators for Machine Learning (CS 217), Stanford University, fall 2018

A (Long) Peek into Reinforcement Learning

**Machine Learning **

Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig

Introduction to Machine Learning by Ethem Alpaydin

Introduction to Machine Learning by Alex Smola (free download)

Pattern Recognition and Machine Learning by Christopher Bishop

Programming Collective Intelligence Toby Segaran

Bayesian Reasoning and Machine Learning by David Barber

Bayesian Data Analysis by Andrew Gelman and John B. Carlin

**Data Analysis**

Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman and Jennifer Hill

The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie and Robert Tibshirani

Statistical Inference by George Casella

Advanced Data Analysis from an Elementary Point of View by Cosma Rohilla Shalizi (draft free download)

**Statics and Probability**

Introductory Statistics Barbara Illowsky,Susan Dean (free download)

OpenIntro Statistics by David M Diez, Christopher D Barr, Mine Cetinkaya-Rundel (free download)

Introduction to Probability by Charles M. Grinstead and J. Laurie Snell

Introduction to Probability Models by Sheldon M. Ross

A First Course in Probability by Sheldon Ross

**Linear Algebra and Calculus**

Active Calculus Matthew Boelkins, David Austin, Steven Schlicker (free download)

Convex Optimization by Stephen Boyd and Lieven Vandenberghe (free download)

Linear Algebra and Its Applications by Gilbert Strang

Linear Algebra by Jim Hefferon

Linear Algebra by David Cherney, Tom Denton and Andrew Waldron (free download)

Linear Algebra Done Wrong by Sergei Treil (free download)

Deep Learning Book Notes, Chapter 2: Linear Algebra for Deep Learning

**Presenting the information**

The Visual Display of Quantitative Information by Edward R. Tufte

**Some of my posts**

Machine Learning Research Groups

Machine Learning and Its evolution