# joelgrus/data-science-from-scratch

code for Data Science From Scratch book

Repository: https://github.com/joelgrus/data-science-from-scratch
Canonical: https://ross.abutalabs.com/products/data-science-from-scratch
Language: Python
License: MIT
License Family: permissive
Last push: 2023-11-09T21:18:24+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": 4316, "days_push": 1028, "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 9705, forks 4716 (observed 2026-08-28T04:10:36.866205+00:00)

## What it is
Companion code repository for the book 'Data Science from Scratch' (2nd edition) by Joel Grus, containing pure-Python implementations of data science fundamentals. It includes a 'scratch' package covering linear algebra, statistics, probability, machine learning, neural networks, and more.

## Use cases
- learn data science fundamentals in pure python
- implement machine learning algorithms from scratch
- understand linear algebra and statistics for data science
- study gradient descent and neural network basics
- follow along with the Data Science from Scratch book
- teach introductory data science with simple python examples

## When to choose
- you are learning data science concepts and want minimal-dependency python code
- you want to see how algorithms like k-NN, regression, and neural nets work internally
- you are reading the book and want runnable examples

## When to avoid
- you need production-grade, performant machine learning libraries
- you want actively developed tooling rather than book companion code
- you need scalable implementations for large datasets

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, machine-learning, math, data-visualization
- domain: data-science, machine-learning, education, tutorials
- platform: python, cross-platform
- tags: book-code, educational, from-scratch-implementations, statistics, linear-algebra, deep-learning-basics

## Member repositories
- joelgrus/data-science-from-scratch (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:36.866205+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 2026-08-29T17:21:14.924682+00:00, confidence not recorded.
  - readme: https://github.com/joelgrus/data-science-from-scratch (fetched 2026-08-28T04:10:36.866205+00:00, sha 5c3dacdc0ab4)
- Data as of 2026-08-30T08:39:29.467469+00:00.