Another "awesome" list, but this one actually tries to be useful

What it does This is a curated directory of over 500 links covering the full data science stack: Python tooling, SQL, visualization, ML, MLOps, career prep, and cheat sheets. It is essentially a human-filtered search engine for a field drowning in tutorials.

The interesting bit The maintainer bothered to categorize resources by function rather than just topic. You will find sections for data quality validation, automated EDA tools, and even “useful VS Code extensions” — the boring infrastructure that actually speeds up daily work. There is also a web version for easier browsing, which suggests someone uses their own product.

Key highlights

• Covers the full pipeline: roadmaps, learning resources, tools, datasets, interview prep, and cheat sheets
• Includes modern alternatives like Polars and Vaex alongside standard Pandas/NumPy
• CC0 license — no attribution friction if you fork or repurpose
• Accepts community suggestions via issues and discussions
• Web-rendered version available for comfortable reading

Caveats

• No quality ratings or maintenance dates on individual links, so rot is inevitable
• Some sections are thin (A/B Testing has no sub-categories at all)
• “500+” claim is unverified; the list is long but not exhaustively counted

Verdict Worth bookmarking if you are learning data science or need to onboard someone quickly. Skip it if you already have a personal knowledge base — this does not replace one.