# ml874/Data-Science-Cheatsheet

Repository: https://github.com/ml874/Data-Science-Cheatsheet
Canonical: https://ross.abutalabs.com/products/ml874-data-science-cheatsheet
Language: TeX
License Family: other
Last push: 2022-09-18T14:59:37+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": 2945, "days_push": 1445, "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 2572, forks 606 (observed 2026-08-28T04:07:01.500657+00:00)

## What it is
A 9-page data science cheatsheet written in TeX covering probability, statistics, statistical learning, machine learning, big data frameworks, SQL, and graph theory. It is a study reference loosely based on The Data Science Design Manual and An Introduction to Statistical Learning.

## Use cases
- review data science concepts before an interview
- quick reference for probability and statistics formulas
- study machine learning fundamentals
- brush up on SQL and big data frameworks
- printable data science study sheet

## When to choose
- you need a compact printable reference for data science fundamentals
- you are preparing for data science interviews
- you want a free summary of statistical learning concepts

## When to avoid
- you need interactive tutorials or runnable code examples
- you need deep, comprehensive coverage of any single topic
- you need a commercial-use resource (CC BY-NC-SA license)

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, data-science
- domain: data-science, machine-learning, tutorials, education
- platform: cross-platform
- tags: cheatsheet, reference-guide, statistics, probability, sql, interview-prep, tex

## Member repositories
- ml874/Data-Science-Cheatsheet (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:01.500657+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 2026-08-30T02:23:09.852255+00:00, confidence not recorded.
  - readme: https://github.com/ml874/Data-Science-Cheatsheet (fetched 2026-08-28T04:07:01.500657+00:00, sha 6404d9012426)
- Data as of 2026-08-30T08:39:29.467469+00:00.