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, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition, by Aurélien Géron

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

Deep Learning with Python 2nd edition by Francois Chollet

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition, by

The Hundred-Page Machine Learning Book by Andriy Burkov

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps by Valliappa Lakshmanan,Sara Robinson, Michael Munn

**Books on General AI-Machine Learning**

**Rebooting AI: Building Artificial Intelligence We Can Trust** by Gary F. Marcus,Ernest Davis

**Artificial Intelligence: A Guide for Thinking Humans **by Melanie Mitchell

**Human Compatible: Artificial Intelligence and the Problem of Control** by Stuart Russell

**Superintelligence: Paths, Dangers, Strategies** by Nick Bostrom

**Life 3.0: Being Human in the Age of Artificial Intelligence** by Max Tegmark

**AI Superpowers: China, Silicon Valley, and the New World Order** by Kai-Fu Lee

**Prediction Machines: The Simple Economics of Artificial Intelligence** by Ajay Agrawal, Joshua Gans,Avi Goldfarb

**Human + Machine: Reimagining Work in the Age of AI** by Paul R. Daugherty H. James Wilson

**The Book of Why: The New Science of Cause and Effect** by Judea Pearl Dana Mackenzie

**Weapons of Math Destruction** by Cathy O’Neil

**The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do** by Erik J. Larson

**The Ai-First Company: How to Compete and Win with Artificial Intelligence** by Ash Fontana

**Real World AI : A Practical Guide for Responsible Machine Learning** by Alyssa Simpson Rochwerger, Wilson Pang

**Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence** by Kate Crawford

**Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World** by Cade Metz

**Framers: Human Advantage in an Age of Technology and Turmoil** by Kenneth Cukier, Viktor Mayer-Schönberger,Francis de Véricourt

**Newsletters:**

Exponential View by Azeem Azhar

Deep Learning Weekly

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

Inside AI by Rob May

Machine Learning Yearning by Andrew Ng

The Sequence of AI Knowledge

**Online courses**

Yann LeCun’s Deep Learning Course at CDS

Full Stack Deep Learning Learn Production-Level Deep Learning from Top Practitioners

Stanford CS224N: Natural Language Processing

Stanford CS221 Artificial Intelligence

Stanford CS221 Artificial Intelligence video Lectures

Stanford CS221 Artificial Intelligence Cheat Sheets

Stanford CS229 Machine Learning

Stanford CS229 Machine Learning video Lectures

Stanford CS229 Machine Learning Cheat Sheets

Stanford CS230: Deep Learning

Stanford CS230 Deep Learning video Lectures

Stanford CS230 Deep Learning Cheat Sheets

Stanford CS234: Reinforcement Learning

Introduction to Deep Learning STAT 157, UC Berkeley, Spring, 2019, instructors Alex Smola and Mu Li

CS294-158 Deep Unsupervised Learning Spring 2019 , Berkeley, Instructors: Pieter Abbeel, Peter Chen, Jonathan Ho, Aravind Srinivas

Advanced Deep Learning and Reinforcement Learning, Deep Mind

Practical Deep Learning For Code – fast.ai Free

Spinning Up in Deep RL OpenAI

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.

Oxford Deep NLP 2017 course

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

Spinning Up in Deep RL, from OpenAI, a lot of info on Reinforcement Learning

Amazon AWS Machine Learning courses

Lex Fridman on YouTube

MIT Deep Learning

MIT GitHub

Backpropagation explained by Lex Fridman

Neural Networks: Feedforward and Backpropagation Explained & Optimization

**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

Mathematics for Machine Learning, by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong

Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto, Second Edition, MIT Press, Cambridge, MA, 2018 (free PDF download)Papers with Code, selected machine learning papers with code

**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 as a service

Weapons of Math Destruction

Machine Learning and Its evolution

Deep Learning

**Data Annotation Tools for machine learning**