# hadley/stats337

Readings in applied data science

Repository: https://github.com/hadley/stats337
Canonical: https://ross.abutalabs.com/products/stats337
Language: R
License: CC-BY-SA-4.0
License Family: other
Topics: teaching, data-science
Last push: 2018-06-21T15:57:29+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": 3082, "days_push": 2995, "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 1612, forks 223 (observed 2026-08-28T04:05:11.053667+00:00)

## What it is
A curated reading list for Stanford's Stats 337 discussion course on applied data science, maintained by Hadley Wickham. It collects papers, blog posts, and weekly course materials covering topics like tidy data, ethics, and collaboration.

## Use cases
- find readings for a data science course
- self-study applied data science
- build a university data science curriculum
- find papers on data science ethics and best practices
- learn about tidy data and reproducible research

## When to choose
- you want a curated, opinionated reading list on applied data science
- you are teaching or designing a data science discussion course
- you want short papers and blog posts rather than textbooks

## When to avoid
- you need executable software or code tools
- you want a systematic, comprehensive survey of data science
- you need up-to-date material, as the course was last updated in 2018

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation
- domain: data-science, education, tutorials
- platform: cli
- tags: course-materials, readings, r, stanford, curriculum

## Member repositories
- hadley/stats337 (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:11.053667+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 2026-08-30T03:51:09.989325+00:00, confidence not recorded.
  - readme: https://github.com/hadley/stats337 (fetched 2026-08-28T04:05:11.053667+00:00, sha f8e103b389df)
- Data as of 2026-08-30T08:39:29.467469+00:00.