alexeygrigorev/data-science-interviews

scikit-learn is an open-source Python module for machine learning at scikit-learn/scikit-learn, layered on top of SciPy and covering classification, regression, clustering, and dimensionality reduction. The project traces back to a 2007 Google Summer of Code effort by David Cournapeau, was known early on as scikits.learn, and is now kept up by a volunteer community with backing from several organizations, distributed under the BSD-3-Clause license.

This is a traditional awesome-list that curates hundreds of Data Science learning resources — from Python/R fundamentals through to deep learning, agents, and MLOps — organized into sections like tutorials, MOOCs, free courses, and a beginner roadmap. It covers the major bases (Pandas, NumPy, Scikit-Learn, data visualization) and goes broader into research tools, LLM/agent frameworks, and interview prep. The tradeoff: it's a link aggregator, not a course — there's no structure to follow beyond what you piece together yourself, and quality of linked resources varies. For someone who already knows what they're looking for, it's a decent bookmark collection; for a complete beginner following a curated path, the sheer volume can be overwhelming without a clear sequence.

Apache Superset is a self-hosted, open source platform for exploring data and building dashboards, pairing a no-code chart builder with a full SQL Editor and a semantic layer for reusable metrics.

30 Days Of Python is a free GitHub curriculum by Asabeneh Yetayeh that breaks Python into 30 daily topics, from syntax basics through web scraping, MongoDB, and building an API. The README pitches it at beginners and working professionals, admits the timeline realistically runs past 100 days, and points to a Telegram group and companion YouTube channel for support.