# academic/awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

Repository: https://github.com/academic/awesome-datascience
Canonical: https://ross.abutalabs.com/products/awesome-datascience
License: MIT
License Family: permissive
Topics: data-science, machine-learning, data-visualization, science, data-mining, awesome-list, deep-learning, analytics, data-scientists, hacktoberfest
Last push: 2026-08-26T17:06:22+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 97, longevity 100
- inputs: {"age_days": 4442, "days_push": 7, "days_rel": 23, "gap_med": 25, "n_releases_24m": 14}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 29874, forks 6614 (observed 2026-08-28T04:11:53.699284+00:00)

## What it is
A curated awesome-list repository of data science learning resources, covering courses, tutorials, algorithms, tools, books, podcasts, and communities. It serves as a roadmap for learning data science and applying it to real-world problems.

## Use cases
- find free data science courses and moocs
- learn data science from scratch with a structured path
- discover machine learning libraries and tools
- find data science books podcasts and communities
- explore deep learning frameworks and ecosystems
- prepare for a data science career

## When to choose
- you want a curated starting point for learning data science
- you need links to courses, tools, and literature in one place

## When to avoid
- you need runnable software or a library
- you want interactive lessons rather than a link collection

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-science, machine-learning, data-visualization
- domain: data-science, machine-learning, tutorials, awesome-lists
- platform: cross-platform
- tags: awesome-list, curated-resources, learning-path, moocs, deep-learning

## Member repositories
- academic/awesome-datascience (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:53.699284+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 2026-08-29T16:52:52.628661+00:00, confidence not recorded.
  - readme: https://github.com/academic/awesome-datascience (fetched 2026-08-28T04:11:53.699284+00:00, sha 0611a92feb49)
- Data as of 2026-08-30T08:39:29.467469+00:00.