Some resouces for getting started with data science and quant. 

Statistics & Regression: 

• Wooldridge - Introductory Econometrics 
• Hastie - Elements of Statistical Learning 
• Shumway - Time Series Analysis and Its Applications 
• Yamane - Statistics for Economists, Courses 
• Lambert - A graduate course in econometrics 
• Cunningham - Causal Inference: The Mixtape 
• Taboga - Statlect

Math

• Peter - Mathematics for Machine Learning 
• Petersen - Matrix Cookbook
• Boyd - Convex Optimization
• Russell - Artificial Intelligence: A Modern Approach 
• Silver - DeepMind x UCL | Introduction to Reinforcement Learning 
• AWS - MLU-Explain 
• Murphy - Probabalistic Machine Learning 

Competitive Programming

• Laaksonen - Competitive Programmer’s Handbook
• Qi - USACO Guide
• Kogler - Algorithms for Competitive Programming
• Problem Banks: CodeForces, AtCoder, Project Euler, Kattis, CodinGame, Grind75

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
• Some internships to apply for

Quant: 

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

Fun Reads: 

• AoPS - Broken Stick variations
• Wildenhain - Microsoft Word is Turing Complete
• Murphy - GradIEEEnt Half Decent
• Kranakis - An interesting queuing problem
• Marx - Calculus