# Chandra0505/Data-Science-Resources

Repository: https://github.com/Chandra0505/Data-Science-Resources
Canonical: https://ross.abutalabs.com/products/data-science-resources
License Family: other
Last push: 2019-03-29T16:25:51+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2818, "days_push": 2714, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1150, forks 547 (observed 2026-08-28T04:03:46.539256+00:00)

## What it is
A curated collection of data science resources including books, cheatsheets, and links to other learning materials. It is community-maintained via pull requests and free for anyone to fork or clone.

## Use cases
- find free data science books
- learn data science from curated resources
- download machine learning cheatsheets
- collect study material for data science interviews
- discover links to data science tutorials
- build a personal data science reading list

## When to choose
- you want a single curated list of data science books and cheatsheets
- you are starting to learn data science and need free materials
- you want to contribute or share learning resources via pull requests

## When to avoid
- you need interactive courses or structured curricula rather than links and PDFs
- you need up-to-date tooling or code rather than reference material
- you require a maintained software library or package

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, developer-tools
- domain: data-science, tutorials, awesome-lists
- platform: cross-platform
- tags: awesome-list, books, cheatsheets, data-science-resources, curated-links

## Member repositories
- Chandra0505/Data-Science-Resources (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:46.539256+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 2026-08-30T06:33:38.419838+00:00, confidence not recorded.
  - readme: https://github.com/Chandra0505/Data-Science-Resources (fetched 2026-08-28T04:03:46.539256+00:00, sha 4fc50b114f11)
- Data as of 2026-08-30T08:39:29.467469+00:00.