# aaronwangy/Data-Science-Cheatsheet

A helpful 5-page machine learning cheatsheet to assist with exam reviews, interview prep, and anything in-between.

Repository: https://github.com/aaronwangy/Data-Science-Cheatsheet
Canonical: https://ross.abutalabs.com/products/data-science-cheatsheet
Language: TeX
License Family: other
Topics: cheatsheet, data-science, machine-learning
Last push: 2023-03-15T22:16:54+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": 2035, "days_push": 1267, "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 5447, forks 757 (observed 2026-08-28T04:09:18.427884+00:00)

## What it is
A 5-page machine learning and data science cheatsheet in PDF form, based on MIT's ML courses 6.867 and 15.072. It summarizes core algorithms and concepts like regression, SVM, clustering, neural networks, and reinforcement learning for quick review.

## Use cases
- review machine learning concepts before an exam
- prepare for data science interview questions
- quickly refresh on algorithms like SVM or random forests
- find a concise ML reference covering a semester of material
- study reinforcement learning and time series fundamentals
- get a printable machine learning summary sheet

## When to choose
- you want a compact, printable summary of core ML algorithms and concepts
- you are preparing for exams or interviews and need quick review material
- you prefer concept-focused references over language-specific tutorials

## When to avoid
- you need hands-on code examples in Python or SQL
- you want a comprehensive textbook or interactive course
- you need coverage of cutting-edge topics like GANs or graph neural networks

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, nlp, data-science, documentation
- domain: machine-learning, data-science, tutorials, education
- platform: cross-platform
- tags: cheatsheet, reference, latex, interview-prep, pdf

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

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:18.427884+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 2026-08-29T17:56:50.586323+00:00, confidence not recorded.
  - readme: https://github.com/aaronwangy/Data-Science-Cheatsheet (fetched 2026-08-28T04:09:18.427884+00:00, sha 915ff3691d96)
- Data as of 2026-08-30T08:39:29.467469+00:00.