# cs109/content

Official content for Harvard CS109

Repository: https://github.com/cs109/content
Canonical: https://ross.abutalabs.com/products/cs109-content
Homepage: http://cs109.org
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2022-12-21T00:11:06+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": 4759, "days_push": 1352, "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 1793, forks 1565 (observed 2026-08-28T04:05:37.215138+00:00)

## What it is
Official course content for Harvard's CS109 Data Science course, including homework assignments, lecture supplements, and labs as Jupyter notebooks. It covers data wrangling, visualization, machine learning, Bayesian statistics, and web scraping, and is freely available for self-study.

## Use cases
- learn data science from scratch
- self-study harvard cs109 course
- practice data science homework with solutions
- learn pandas and matplotlib with jupyter notebooks
- learn bayesian statistics and cross-validation
- find data science course assignments and labs

## When to choose
- you want a free, structured university-level data science curriculum
- you learn best from hands-on Jupyter notebook assignments with solutions
- you want coverage of classic data science topics like EDA, regression, and Bayesian methods

## When to avoid
- you need up-to-date course material - the content dates from 2013-2015
- you want deep learning or modern LLM topics, which are in CS109B instead
- you need a maintained library or tool rather than educational content

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, machine-learning, data-visualization, web-scraping, nlp
- domain: data-science, education, tutorials, machine-learning
- platform: python, cross-platform
- tags: jupyter-notebooks, harvard, course-material, assignments, labs, statistics, pandas, scikit-learn, bayesian

## Member repositories
- cs109/content (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:37.215138+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 2026-08-30T03:22:56.109924+00:00, confidence not recorded.
  - readme: https://github.com/cs109/content (fetched 2026-08-28T04:05:37.215138+00:00, sha e602fed86d60)
  - homepage: http://cs109.org (fetched 2026-08-29T11:01:52.228068+00:00, sha 4e71d41c6002)
- Data as of 2026-08-30T08:39:29.467469+00:00.