# scipy-lectures/scientific-python-lectures

Tutorial material on the scientific Python ecosystem

Repository: https://github.com/scipy-lectures/scientific-python-lectures
Canonical: https://ross.abutalabs.com/products/scipy-lectures-scientific-python-lectures
Homepage: https://lectures.scientific-python.org
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-05-01T13:56:44+00:00

## Health v2 (maintenance only)
Score: 59/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 80, release rhythm 8, longevity 100
- inputs: {"age_days": 5950, "days_push": 124, "days_rel": 491, "gap_med": null, "n_releases_24m": 1}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3216, forks 1205 (observed 2026-08-28T04:07:49.583779+00:00)

## What it is
A comprehensive set of tutorial lectures on the scientific Python ecosystem, covering NumPy, SciPy, Matplotlib, scikit-learn, scikit-image, and advanced topics, built with Jupyter Book. It is designed as full course material for scientific computing with Python, from beginner to expert level.

## Use cases
- learn scientific computing with python
- numpy and scipy tutorial for beginners
- course material for teaching python for data science
- learn matplotlib plotting
- introduction to machine learning with scikit-learn
- learn image processing with python
- optimize and debug python scientific code

## When to choose
- you want a free, well-maintained full course on the scientific Python ecosystem
- you are an instructor looking for reusable teaching material
- you are a beginner moving toward intermediate/advanced scientific Python topics

## When to avoid
- you need interactive graded exercises or a MOOC platform
- you want documentation for a specific library version rather than tutorial content
- you need a non-Python scientific computing curriculum

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, data-science, machine-learning, image-processing, math
- domain: tutorials, data-science, education, machine-learning, developer-tools
- platform: python, cross-platform
- tags: numpy, scipy, matplotlib, jupyter-book, scientific-computing, course-material, open-textbook

## Member repositories
- scipy-lectures/scientific-python-lectures (main) score 59

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:49.583779+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 2026-08-29T18:44:56.033107+00:00, confidence not recorded.
  - readme: https://github.com/scipy-lectures/scientific-python-lectures (fetched 2026-08-28T04:07:49.583779+00:00, sha b2d9543d9a3c)
  - homepage: https://lectures.scientific-python.org (fetched 2026-08-29T09:38:06.650623+00:00, sha 8452c7b6df94)
  - site_page: https://lectures.scientific-python.org/about.html (fetched 2026-08-29T09:38:06.662058+00:00, sha 5da6c747d5df)
  - site_page: https://lectures.scientific-python.org/intro/help/help.html (fetched 2026-08-29T09:38:06.659672+00:00, sha 56a77e614f94)
- Data as of 2026-08-30T08:39:29.467469+00:00.