A lot of people have asked how they can learn more about data science and quant, so I thought I'd put together some resouces. 

Statistics & ML: 

• Wooldridge - Introductory Econometrics 
• Hastie - Elements of Statistical Learning 
• Shumway - Time Series Analysis and Its Applications 
• Peter - Mathematics for Machine Learning 
• Yamane - Courses 
• Lambert - A graduate course in econometrics 
• Russell - Artificial Intelligence: A Modern Approach 
• Silver - DeepMind x UCL | Introduction to Reinforcement Learning 
• Cunningham - Causal Inference: The Mixtape 
• AWS - MLU-Explain 
• Taboga - Statlect
• Petersen - Matrix Cookbook

Quant: 

• Bali - Empirical Asset Pricing
• Dama - On Automated Trading
• Mudders are invited to join Harvey Mudd Data Science

Brainteaser Collections: 

• Zhou - A Practical Guide To Quantitative Finance Interviews
• Crack- Heard on The Street
• Mosteller - Fifty Challenging Problems in Probability
• Winkler - Mathematical Puzzles
• Mathai - An Introduction to Geometrical Probability
• Lehoczky - The Art of Problem Solving

Competitive Programming

• Laaksonen - Competitive Programmer’s Handbook
• Qi - USACO Guide
• Kogler - Algorithms for Competitive Programming
• Problem Banks: CodeForces, AtCoder, Project Euler, Kattis, CodinGame

Fun Reads: 

• Hofstadter - Gödel, Escher, Bach
• Demaine - Every Author as First Author
• AoPS - Broken Stick variations
• ACH - SIGBOVIK
• Wildenhain - Microsoft Word is Turing Complete
• Murphy - GradIEEEnt Half Decent
• Kranakis - The Urinal Problem
• Marx - Calculus