"AI and Data Scientist Roadmap" problem

[Yah1d0]

Roadmap URL

https://roadmap.sh/ai-data-scientist

Suggestions

1. Resources: Most linked resources are courses, whereas other roadmaps (e.g. Python, Machine Learning) mainly link to free docs, articles and tutorials. Examples: Coding, Mathematics nodes.
2. Granularity: Several steps are very broad (e.g. Learn Python: Kaggle Course, Google's Python Class Course) and don't break down into concrete topics such as NumPy/Pandas, scikit-learn, PyTorch/TensorFlow.
3. Overlap: The Python, Machine Learning and MLOps roadmaps cover this path in more practical detail.

Suggestion: Replace course links with free alternatives where possible (official docs, Kaggle Learn, fast.ai, PyTorch tutorials), and split broad nodes into concrete topics. Happy to help with a PR if this direction makes sense, if im wrong sorry for interrupting.

[pardeep1916P]

Hey @zhan1417,

Thanks for raising this! I completely agree that the foundational nodes in this roadmap (especially Mathematics and Coding) needed richer, free learning resources and proper categorization.

I've opened PR #10416 to address the resource gaps:

• Added Khan Academy courses, 3Blue1Brown visual series, and MIT OCW / MML open-access textbooks for Mathematics (covering both Linear Algebra & Calculus).
• Fixed the link tag ordering in Coding, corrected the Harvard course tag, and added Jake VanderPlas's open-access Python Data Science Handbook.

Regarding splitting nodes into subtopics: in this repository, roadmap nodes and IDs are tied to the roadmap canvas editor and database, so content contributions update existing nodes. The maintainers can review the canvas structure directly if they decide to split the nodes.