You Don't Need Another Tab Open: 500+ Data Analysis Resources in One Place

You've got seventeen browser tabs open right now. One is a pandas cheatsheet, another is a SQL roadmap someone tweeted, and there's a half-read article on A/B testing you keep meaning to get back to. Bookmarking things is easy; finding them again when you actually need them is the hard part. Awesome Data Analysis is a curated collection of over 500 resources for data analysis and data science, organized so you can stop hoarding links and start using them.

What It Does

At its core, this is an awesome list, which means it's a curated index of links rather than a piece of software. The repository gathers tools, libraries, roadmaps, cheatsheets, interview guides, and learning materials covering the data analysis and data science landscape. Everything is broken into a detailed table of contents, so you can jump straight to the area you care about instead of scrolling through a wall of links.

The categories are broad but specific enough to be useful. You'll find sections on Python (with subcategories for pandas and NumPy, data processing and transformation, automated EDA, data quality and validation, feature engineering, and specialized tools), SQL and databases, data visualization, dashboards and BI, web scraping, mathematics, statistics and probability, A/B testing, time series analysis, data engineering, NLP, machine learning, MLOps, AI applications, cloud platforms, system design, productivity, and skill development and career resources. Each major section is typically split into resources and tools, so you can separate the things you read from the things you install.

The project also ships with a web version hosted on GitHub Pages, which is worth knowing about if you'd rather browse in a cleaner reading environment than the GitHub file viewer.

Why It's Cool

• The structure is the feature. Anyone can dump 500 links into a README. What makes this useful is the taxonomy. Splitting Python into data manipulation, automated EDA, validation, and feature engineering means you can find the right tool for a specific job instead of wading through a generic "Python tools" list.

• It covers the whole pipeline, not just the modeling part. Plenty of data science lists stop at machine learning libraries. This one goes from statistics and mathematics through data engineering, MLOps, cloud infrastructure, and system design. That's a more honest picture of what working with data actually involves.

• Interview guides and roadmaps are first-class citizens. There's a dedicated roadmaps section and a skill development and career section with practice resources. If you're trying to figure out what to learn next or prepping for interviews, those sections exist alongside the tooling lists rather than being an afterthought.

• It's licensed CC0. Public domain dedication means you can copy, remix, or reuse the list however you want. For a community resource, that's the right call.

• The contribution path is open and low-friction. There's a dedicated issue for suggestions and a Discussions tab, so if you spot a gap, you don't have to guess at the maintainer's preferred process.

• The web version exists. It's a small thing, but having a hosted page means you can share a link with a colleague who doesn't live on GitHub and they'll actually be able to read it.

How to Try It

There's nothing to install. This is a list, so "trying it" means browsing.

1. Start with the repository: github.com/pavelgrigoryevds/awesome-data-analysis
2. If you prefer a cleaner reading experience, use the web version: pavelgrigoryevds.github.io/awesome-data-analysis
3. Jump to the section that matches what you're working on right now (the table of contents is thorough, so use it)
4. If you find something missing or broken, open an issue or start a discussion

If you want to contribute, the README points to a specific suggestion issue and the Discussions tab. PRs are explicitly welcome.

Final Thoughts

Awesome lists live or die by curation, and this one has clearly put thought into its category structure rather than just alphabetizing links. It's not going to teach you data analysis, and it won't replace a good course or a real project. What it will do is give you a map of the territory, which is genuinely useful when you're not sure what you don't know yet.

This is best for people who are early-to-mid in their data career, or anyone who keeps accumulating resources without a system for organizing them. If you're a seasoned practitioner with a settled toolkit, you'll probably recognize most of what's here, though the breadth across adjacent areas like MLOps and system design might still surface something new. Either way, it's a solid reference to keep around.