// the find
krishnaik06/6-Months-Data-Science-Roadmap-
This is a link roundup, not a codebase — a single README from Krish Naik listing month-by-month topics (Python, stats, SQL, ML, DL, NLP, MLOps) with YouTube playlist links to learn data science over six months. It's aimed at complete beginners who want a syllabus and are fine consuming hours of video lectures rather than reading a book or doing a structured course.
The topic sequencing is sound and matches how data science teams actually hire junior candidates — Python fundamentals before stats, stats before ML, ML before deployment. Coverage extends past model training into CI/CD, Docker/Kubernetes, MLflow, Airflow and monitoring, which most beginner roadmaps skip entirely. Content is offered in both English and Hindi, which is a real accessibility win for a large chunk of the target audience. It's free, and the playlists linked are long-running series with dozens of videos each rather than a handful of teaser clips.
There is no code, no notebooks, no exercises, and no repo structure to speak of — it's a README pointing entirely at third-party YouTube content the maintainer doesn't control, so link rot and quality drift are inevitable over time. The 'What's New In Python 3.10' section and general framing are already stale relative to current Python and tooling versions, and the repo hasn't been pushed to since January 2024. The bottom of the README pivots into promoting a paid iNeuron/PW Skills course, which undercuts the 'free roadmap' framing. There's no way to track progress or verify learning outcomes beyond an external Google Drive spreadsheet, and nothing here is testable or forkable in the way an actual project-based curriculum would be.