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ds-cheatsheets: Find Data Science Reference Sheets

A curated collection of data science cheat sheets covering Python, R, statistics, machine learning, deep learning, big data, SQL, and visualization. Use it to quickly find practical PDF and image references across common tools and workflows.

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Overview

ds-cheatsheets is a browsable collection of reference sheets for data science concepts, programming tools, and workflows. It brings links to materials from multiple sources into one categorized repository, helping practitioners look up syntax or refresh concepts without searching across many sites.

The collection is most useful as a quick-reference index, not as a structured course or a single, maintained documentation set. Many entries link to PDFs and images, including resources hosted in other repositories.

Key Features

  • Cheat sheets for Python fundamentals, pandas, NumPy, Jupyter, regular expressions, and visualization libraries.
  • R references for base R, tidyverse workflows, data transformation, dates, strings, and package development.
  • Statistics and mathematics refreshers covering probability, algebra, calculus, and statistics.
  • Machine learning and deep learning references for supervised and unsupervised learning, model selection, neural networks, and frameworks including scikit-learn and Keras.
  • Big data resources covering PySpark, Dask, and Sparklyr.
  • Additional references for SQL, Git, Bash, DVC, Vim, and Emacs.
  • Links to PDFs alongside preview images for many resources.

Use Cases

  • A Python or R practitioner needs a quick syntax reminder while working with data preparation or visualization libraries.
  • A learner wants concise refreshers on probability, statistics, machine learning, or deep learning alongside other study materials.
  • A data scientist starting a project needs to locate references for tools such as Jupyter, SQL, PySpark, or Git.
  • An instructor or team lead wants a collection of external cheat sheets to share as supplementary references.

Getting Started

Browse the README and follow its table of contents to open a cheat sheet. Most listed references are PDFs or images; there is no installation step described.

Considerations

  • This is a collection of links and reference files, not an executable data science toolkit or a step-by-step curriculum.
  • Resources come from multiple authors and repositories, so coverage, terminology, and update schedules may differ between entries.
  • Some links point to external repositories, and their availability or content may change independently of this project.
  • The repository is not marked as archived, but the README includes no guidance about validating whether each linked resource is current.

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