# DataScienceSpecialization/courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

Repository: https://github.com/DataScienceSpecialization/courses
Canonical: https://ross.abutalabs.com/products/datasciencespecialization-courses
Language: HTML
License Family: other
Last push: 2021-03-30T06:51:57+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": 4607, "days_push": 1982, "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 4151, forks 30937 (observed 2026-08-28T04:08:36.488311+00:00)

## What it is
Course materials for the Johns Hopkins Data Science Specialization offered on Coursera. The repository contains lecture notes, assignments, and supporting content written primarily in HTML/R Markdown.

## Use cases
- learn data science for free
- study the JHU data science specialization outside Coursera
- find course materials for R and machine learning classes
- self-study a data science curriculum
- reference lecture notes for statistics and data analysis

## When to choose
- you want free, self-paced access to a well-known university data science curriculum
- you prefer reading course materials without enrolling on Coursera
- you want R-based examples and assignments for learning data science

## When to avoid
- you need actively maintained or updated course content
- you want interactive graded assignments or certificates
- you need a software tool rather than educational material

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, documentation
- domain: data-science, education, tutorials
- platform: cross-platform
- tags: coursera, johns-hopkins, r, course-materials, open-courseware

## Member repositories
- DataScienceSpecialization/courses (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:36.488311+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 2026-08-29T18:22:53.816090+00:00, confidence not recorded.
  - readme: https://github.com/DataScienceSpecialization/courses (fetched 2026-08-28T04:08:36.488311+00:00, sha c80c4aaf9a40)
- Data as of 2026-08-30T08:39:29.467469+00:00.