Resources for Quant, Deep Learning & Large-Scale Data

This is a living list — not a one-time post. I'm collecting the books, courses, videos, hands-on practice, project ideas, posts, and subreddits I actually use while working on quant, deep learning, and large-data problems, and I'll keep adding to it as I find things worth sharing. No publish date on this one since it's meant to keep growing — check back for updates.

Interview Prep & Forever Learning

"A healthy mind in a healthy body."
— the famous Latin phrase mens sana in corpore sano, coined by the Roman poet Juvenal

I believe the mind needs daily training just like the body does, to stay nourished, sharp, and healthy. In the era of AI, it matters more than ever to keep our minds engaged with social and logical challenges.

These are my daily doses, whether I'm preparing for interviews or just sharpening my mind, so I'm ready when the opportunity comes:

Mental Math Trainer — a simple trainer for addition, subtraction, multiplication, and division, just faster and more competitive, so it feels like a game. Ten minutes a day keeps the calculator away!
Wordle — super fun. I started playing it at work with my manager. With friends, colleagues, or on your own, word guessing builds vocabulary and sharpens your thinking.
TraderMath — now we're getting technical. One of the best sites I've used: its courses, challenges, and quant-firm-style questions help me with advanced probabilistic thinking and keep me ready for online assessments and interviews.
NeetCode — you just can't escape LeetCode, so grind it every day. It helps with online assessments and technical rounds, makes you a better programmer, and helps a ton with critical thinking.

Books

Forecasting: Principles and Practice (with Python) — the Python edition of the standard open-access forecasting reference. Covers the full toolkit of good forecasting techniques, from classical decomposition and exponential smoothing through to more modern approaches, with runnable code throughout.

Courses

Stock Market Analysis Zoomcamp by Ivan Brigida — a hands-on course covering:
- Key techniques for collecting and cleaning historical market data using free and paid APIs
- How to merge time series datasets using Pandas and Polars for quantitative analysis
- Training classical machine learning models for stock movement and financial forecasting
- Designing end-to-end backtesting engines to validate trading strategies before live deployment
- Practical ways to leverage LLM coding assistants without compromising system execution accuracy
- Requirements for submitting capstone projects and navigating the peer review certification process

Videos

Nothing here yet — I'll add videos as I come across good ones.

Hands-On Practice

Numerai — a real-money quant tournament where you submit predictions on obfuscated financial data. Good way to stress-test models against live markets without needing your own capital or trading infrastructure.

More categories — project ideas, posts worth reading, subreddits — will get added here as this list grows. Have something I should check out? Reach out.