# krishnaik06/The-Grand-Complete-Data-Science-Materials

Repository: https://github.com/krishnaik06/The-Grand-Complete-Data-Science-Materials
Canonical: https://ross.abutalabs.com/products/the-grand-complete-data-science-materials
Language: Python
License: GPL-2.0
License Family: copyleft
Last push: 2024-08-04T07:14:11+00:00

## Health v2 (maintenance only)
Score: 28/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 77
- inputs: {"age_days": 1079, "days_push": 759, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9064, forks 4514 (observed 2026-08-28T04:10:28.197764+00:00)

## What it is
A curated collection of video playlists, tutorials, and learning materials covering the full data science journey, from Python and statistics to machine learning, deep learning, NLP, and production deployment. It serves as a structured roadmap with links to free educational content in English and Hindi.

## Use cases
- learn data science from scratch
- find a data science roadmap with videos
- study machine learning and deep learning tutorials
- prepare for data science interviews
- learn Python and SQL for data analytics
- learn MLOps and model deployment

## When to choose
- you want a free, structured, video-based path through data science topics
- you prefer curated playlists over assembling your own resources
- you want content in English or Hindi

## When to avoid
- you need runnable software or a code library
- you want a single authoritative course rather than a link collection
- you need up-to-date materials for fast-moving topics

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, data-science, nlp, deep-learning, etl
- domain: data-science, machine-learning, tutorials, education
- platform: python, cross-platform
- tags: curated-links, video-playlists, roadmap, interview-prep, self-learning

## Member repositories
- krishnaik06/The-Grand-Complete-Data-Science-Materials (main) score 28

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:28.197764+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:24:01.988535+00:00, confidence not recorded.
  - readme: https://github.com/krishnaik06/The-Grand-Complete-Data-Science-Materials (fetched 2026-08-28T04:10:28.197764+00:00, sha 0a584db905d4)
- Data as of 2026-08-30T08:39:29.467469+00:00.