# cs109/2015

Public material for CS109

Repository: https://github.com/cs109/2015
Canonical: https://ross.abutalabs.com/products/2015
License Family: other
Last push: 2022-12-22T02:33:40+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4062, "days_push": 1351, "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 1484, forks 2516 (observed 2026-08-28T04:04:51.389399+00:00)

## What it is
Public repository of course materials for Harvard's CS109 Data Science course (2015 edition), containing lecture slides and links to labs and homework. It serves as a free learning resource covering Python, pandas, git workflows, and data science fundamentals.

## Use cases
- learn data science from Harvard's CS109 course
- find CS109 2015 lecture slides
- practice with CS109 lab notebooks and homework
- learn pandas and python for data analysis
- self-study a university data science curriculum
- learn git workflow through course labs

## When to choose
- you want free, structured university-level data science course material
- you are self-studying Python, pandas, and git for data science
- you want the original 2015 CS109 lectures and assignments

## When to avoid
- you need a software tool or library rather than course content
- you want up-to-date material reflecting current data science tooling
- you need official support, grading, or access to student homework repos

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, developer-tools
- domain: data-science, education, tutorials
- platform: python, cross-platform
- tags: harvard-cs109, course-material, lecture-slides, jupyter-notebooks, data-science-course, pandas, python-learning

## Member repositories
- cs109/2015 (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:51.389399+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 2026-08-30T04:33:59.765020+00:00, confidence not recorded.
  - readme: https://github.com/cs109/2015 (fetched 2026-08-28T04:04:51.389399+00:00, sha 5542ecc26e64)
- Data as of 2026-08-30T08:39:29.467469+00:00.