Free data and AI learning resources that are genuinely good

How these were chosen

Every resource here meets four tests. It's free to use without a trial that runs out. It's maintained by the tool's makers or a long-standing educational project. It teaches through exercises, not only videos. And it's still online: each link was checked in September 2026. Paid certificates attached to some of these are optional and outside the scope of this list.

Python and pandas

[The Python Tutorial](https://docs.python.org/3/tutorial/) (python.org). Written by the people who maintain the language. It's dense, but accurate, and it doesn't teach bad habits. Good for: data structures, functions, modules, errors. Skips: anything specific to data analysis. Work through sections 3 to 9 with the interpreter open and type every example yourself.

[10 minutes to pandas](https://pandas.pydata.org/docs/user_guide/10min.html) (pandas documentation). A fast tour of selecting, grouping, merging and reshaping. It takes longer than ten minutes if you do it properly, so plan for an evening. Good for: learning the vocabulary before a real dataset. Skips: why you would choose one method over another. For that, read the linked user-guide pages on the operations you use most.

SQL

[SQLBolt](https://sqlbolt.com/). Short interactive lessons that run in the browser, from SELECT to joins and aggregates. Good for: your first two weeks of SQL with no setup. Skips: window functions and the query patterns interviews test. Move on once you finish it.

[SQLite language reference](https://www.sqlite.org/lang.html). The authoritative reference for the SQL dialect built into Python's sqlite3 module, so you can practise anywhere Python is installed. Good for: checking exact syntax for window functions, CTEs and date functions. Skips: teaching. Use it as a reference, not a course.

Statistics

[Khan Academy: Statistics and probability](https://www.khanacademy.org/math/statistics-probability). Patient explanations with practice problems and instant checks. Good for: distributions, sampling, confidence intervals and hypothesis tests, especially if school maths feels distant. Skips: applying these ideas to messy business data. Pair each unit with a small pandas exercise on data you make yourself.

Machine learning

[Kaggle Learn](https://www.kaggle.com/learn). Short, hands-on micro-courses that run in browser notebooks, covering Python, pandas, intro machine learning, feature engineering and more. Good for: a first working model without any installation. Skips: careful validation design and the reasoning behind choices. Treat its notebooks as starting points, not finished projects.

[Google Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course). A structured introduction to core ML ideas, including loss, overfitting, classification metrics and data preparation, with interactive exercises. Good for: the concepts behind the code. Skips: building an end-to-end project of your own.

Power BI

[Microsoft Learn: Power BI training](https://learn.microsoft.com/en-us/training/powerplatform/power-bi). Official modules from Microsoft on getting data, modelling, DAX and reports. Good for: learning the tool as its makers intend, and preparing for Microsoft's certification if you want it. Skips: judgement about what a business user actually needs from a dashboard.

Git and the working environment

[Pro Git](https://git-scm.com/book/en/v2) (git-scm.com). The full book on Git, free online. Chapters 1 to 3 are enough to start: basics, branching, and what a commit really is. Good for: understanding instead of memorising commands. Skips: nothing important, though it's more than a beginner needs at first.

[GitHub Docs: Get started](https://docs.github.com/en/get-started). How to create repositories, push code and write a README on GitHub. Good for: putting portfolio work online properly.

[The Missing Semester of Your CS Education](https://missing.csail.mit.edu/) (MIT). Lectures and exercises on the shell, editors, version control and debugging, the tools courses assume you already know. Good for: anyone who freezes at a terminal. Skips: data content. It's purely about tools.

Put them in an order

What no free resource gives you

Every item here explains and checks your answers to exercises its authors wrote. None of them looks at your project and tells you your validation leaked, your chart misleads, or your README can't be followed. Find that feedback somewhere: a colleague, a study group or a mentor. Without it, you can finish every resource on this list and still repeat the same mistakes in interviews.

To see how far free material alone might take you, read self-study roadmap vs structured course. If you want mentor review on projects, the Data Science programme runs live online, with optional in-person labs at our Noida centre.