# datasciencemasters/go

The Open Source Data Science Masters

Repository: https://github.com/datasciencemasters/go
Canonical: https://ross.abutalabs.com/products/datasciencemasters-go
License: Unlicense
License Family: permissive
Last push: 2023-12-03T11:42:49+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": 4767, "days_push": 1004, "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 26268, forks 6107 (observed 2026-08-28T04:11:46.709684+00:00)

## What it is
The Open Source Data Science Masters is a curated curriculum of free resources for self-teaching data science, originally created in 2013. It aggregates courses, books, and materials covering data science fundamentals, machine learning, and the ethics of data-driven work.

## Use cases
- learn data science for free
- self-study curriculum for becoming a data scientist
- find courses and books on machine learning
- structured alternative to a data science degree
- learn about ethics and societal impacts of data science

## When to choose
- you want a free, curated path to learn data science on your own
- you prefer self-directed learning over paid bootcamps
- you want historical context and ethics woven into your data science education

## When to avoid
- you need up-to-date course links and current tooling, as the curriculum dates to 2013
- you want interactive exercises or mentorship rather than a reading list
- you need a credential or certificate

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation
- domain: data-science, tutorials, education
- platform: cross-platform
- tags: curriculum, self-study, open-education, data-science-masters

## Member repositories
- datasciencemasters/go (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:46.709684+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 2026-08-29T16:55:54.107534+00:00, confidence not recorded.
  - readme: https://github.com/datasciencemasters/go (fetched 2026-08-28T04:11:46.709684+00:00, sha d2b241ae970e)
- Data as of 2026-08-30T08:39:29.467469+00:00.