# rushter/data-science-blogs

A curated list of data science blogs

Repository: https://github.com/rushter/data-science-blogs
Canonical: https://ross.abutalabs.com/products/data-science-blogs
Homepage: http://rushter.com/dsreader/
Language: Python
License Family: other
Topics: data-science, machine-learning
Last push: 2024-06-05T09:47:02+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": 4046, "days_push": 819, "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 6340, forks 1756 (observed 2026-08-28T04:09:41.851441+00:00)

## What it is
A curated directory of data science and machine learning blogs, each entry linking to the blog and its RSS/Atom feed. It is a reference list (awesome-list style) rather than runnable software, written in Python only for tooling around the list.

## Use cases
- find good data science blogs to follow
- discover machine learning blog RSS feeds
- populate an RSS reader with analytics and ML blogs
- keep up with data science reading and tutorials
- find blogs from practitioners like Andrej Karpathy and Andreas Müller

## When to choose
- You want a hand-picked, quality-filtered directory of data science blogs with direct RSS links
- You need feed URLs to aggregate data science content in a reader or custom pipeline

## When to avoid
- You need a tool or library rather than a list of links
- You expect frequently updated content — the list is maintained infrequently

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, documentation
- domain: data-science, machine-learning, awesome-lists, tutorials
- platform: -
- tags: awesome-list, curated-list, blogs, rss-feeds, reading-list, learning-resources

## Member repositories
- rushter/data-science-blogs (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:41.851441+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 2026-08-29T17:46:08.796208+00:00, confidence not recorded.
  - readme: https://github.com/rushter/data-science-blogs (fetched 2026-08-28T04:09:41.851441+00:00, sha 4aeaf024dfa9)
- Data as of 2026-08-30T08:39:29.467469+00:00.