Resources for Quant, Deep Learning & Large-Scale Data — A Living List

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.

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.