# jonathan-bower/DataScienceResources

Open Source Data Science Resources.

Repository: https://github.com/jonathan-bower/DataScienceResources
Canonical: https://ross.abutalabs.com/products/datascienceresources
License Family: other
Last push: 2024-02-21T11:03:26+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4340, "days_push": 924, "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 4214, forks 1479 (observed 2026-08-28T04:08:39.155095+00:00)

## What it is
A curated collection of open-source data science resources covering courses, tools, machine learning, data visualization, and career guidance. It is a link repository rather than runnable software, aimed at learners and practitioners.

## Use cases
- find resources to learn data science from scratch
- discover machine learning and deep learning tutorials
- look up data science career path advice
- find data visualization and EDA tools
- explore data science blogs, books, and conferences
- learn statistics and Python for data work

## When to choose
- you want a curated starting point for learning data science
- you need links to courses, tools, and career resources in one place
- you are exploring the breadth of the data science field beyond just coding

## When to avoid
- you need runnable software or a library to install
- you want up-to-date, actively maintained tooling recommendations
- you need a structured course rather than a link collection

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools
- domain: data-science, machine-learning, tutorials, awesome-lists
- platform: cross-platform
- tags: curated-list, awesome-list, career-resources, data-engineering, education

## Member repositories
- jonathan-bower/DataScienceResources (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:39.155095+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 2026-08-29T18:22:27.393709+00:00, confidence not recorded.
  - readme: https://github.com/jonathan-bower/DataScienceResources (fetched 2026-08-28T04:08:39.155095+00:00, sha 802644bcb065)
- Data as of 2026-08-30T08:39:29.467469+00:00.