// the find
krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025
A README-only roadmap listing YouTube playlists and links covering Python, statistics, ML, deep learning, NLP, and MLOps tools, aimed at absolute beginners who want a linear syllabus to follow through 2025. It is not a codebase or library — there is no code in the repo at all, just a curated list of external video links organized by topic.
The topic ordering is sound and matches how most working data scientists actually learned the stack: Python fundamentals before statistics, statistics before ML, ML before deep learning/NLP, and MLOps bolted on at the end rather than upfront. It covers the unglamorous but necessary middle ground (Flask, Docker, MLflow, DVC, CI/CD) that a lot of 'become a data scientist' lists skip in favor of just modeling. Bilingual tracks (English/Hindi) for most sections lower the barrier for a large chunk of the intended audience.
There is zero code, no example project, no `requirements.txt`, nothing to clone and run — this is a link dump, not a resource you install or build on. Every single link points to the same YouTube channel, so this is really 'watch one person's back catalog in order' rather than a survey of the best available material; if that instructor's style doesn't click for you, the roadmap offers no alternative. Content rot is a real risk: several links are already dead or malformed (a nested markdown link inside a badge link under the MLflow entry), and 'In 2025' framing tied to a specific year means the roadmap will need annual upkeep or go stale fast. There's no way to track progress or verify learning — no checkpoints, exercises, or quizzes, just a flat list of hours of video.