awesome-repositories.com
Blog
MCP
awesome-repositories.com

Discover the best open-source repositories with AI-powered search.

ExploreCurated searchesOpen-source alternativesSelf-hosted softwareBlogSitemap
ProjectMCP serverAboutHow we rankPress
LegalPrivacyTerms
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
aaronwangy avatar

aaronwangy/Data-Science-Cheatsheet

0
View on GitHub↗
5,422 stars·756 forks·TeX·14 views

Data Science Cheatsheet

This project is a data science reference sheet and machine learning study guide. It provides a curated collection of formulas, definitions, and model summaries designed for quick lookup during project development and technical interview preparation.

The resource is delivered as a static PDF educational resource. It organizes complex technical frameworks and theoretical machine learning concepts into a portable, fixed-layout document to ensure consistent visual presentation across different devices.

The content covers machine learning concept references and data science knowledge synthesis, specifically tailored for exam review and the synthesis of theoretical frameworks.

Features

  • Data Science Concepts - Looking up quick definitions and formulas for common machine learning models during active project development or study.
  • Technical Reference Sheets - Serves as a curated collection of formulas, definitions, and model summaries for quick lookup during project development.
  • Machine Learning Concepts - Acts as a reference for the fundamental mathematical and structural principles of machine learning models.
  • Interview Reviews - Provides curated reviews of machine learning algorithms and theory specifically tailored for technical interview preparation.
  • Machine Learning Algorithm Study Guides - Provides a condensed study guide of machine learning algorithms and theoretical concepts for exam and interview prep.
  • Automated Knowledge Synthesis Tools - Synthesizes complex theoretical frameworks into structured, high-density summaries for rapid information retrieval.
  • Curated Review Notes - Provides condensed academic summaries of mathematical frameworks and algorithms designed for rapid conceptual review.
  • Educational Topic Partitioning - Organizes theoretical machine learning concepts into discrete categories for targeted information retrieval and structured study.
  • Exam Revision Summaries - Offers condensed summaries of key data science topics for rapid revision before formal academic tests.
  • PDF Compilations - Delivers a downloadable PDF compilation of technical knowledge for offline study.
  • Exam Review Materials - Provides condensed summaries of data science topics for rapid revision before academic exams.

Star history

Star history chart for aaronwangy/data-science-cheatsheetStar history chart for aaronwangy/data-science-cheatsheet

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

AI search

Explore more awesome repositories

Describe what you need in plain English — the AI ranks thousands of curated open-source projects by relevance.

Start searching with AI

Projects sharing features with Data Science Cheatsheet

These projects share indexed features with Data Science Cheatsheet. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • visualize-ml/book7_visualizations-for-machine-learningVisualize-ML avatar

    Visualize-ML/Book7_Visualizations-for-Machine-Learning

    3,290View on GitHub↗

    This project is an educational collection of interactive Jupyter notebooks designed to illustrate fundamental machine learning algorithms and mathematical principles. It serves as a resource for bridging the gap between abstract equations and practical implementation through a combination of narrative text and executable code. The collection utilizes a modular architecture where individual algorithm implementations are isolated to facilitate independent study. It incorporates both interactive code examples and static graphical assets to represent complex statistical concepts and model behavio

    Jupyter Notebookbaysiandata-sciencelinear-algebra
    View on GitHub↗3,290
  • sreeharierk/datasciencesreeharierk avatar

    sreeharierk/datascience

    5,156View on GitHub↗

    This project is a curated knowledge base and learning resource for data science and artificial intelligence. It provides a structured set of curricula, technical notes, and learning paths covering the mathematics, statistics, and algorithms required to build intelligent systems. The repository includes a catalog of open-source projects and practical implementations for deep learning, computer vision, and natural language processing. It also maintains a directory of university courseware and online modules focused on machine learning and robotics. The content covers theoretical foundations in

    artificial-intelligencecomputer-visiondata-science
    View on GitHub↗5,156
  • jonkrohn/ml-foundationsjonkrohn avatar

    jonkrohn/ML-foundations

    4,772View on GitHub↗

    ML-foundations is a machine learning educational curriculum and computer science study guide. It provides a structured learning path focused on the mathematical foundations and computational prerequisites required for studying machine learning. The project serves as a Python mathematics course, delivering interactive notebooks and coding exercises to teach linear algebra, calculus, and statistics. It translates abstract mathematical formulas into concrete algorithmic code to help learners understand the principles underpinning machine learning algorithms. The curriculum covers data science p

    Jupyter Notebookcalculuscomputer-sciencedata-science
    View on GitHub↗4,772
  • josephmisiti/awesome-machine-learningjosephmisiti avatar

    josephmisiti/awesome-machine-learning

    72,867View on GitHub↗

    This project is a comprehensive, community-driven directory of machine learning resources, software libraries, and educational materials. It serves as a centralized knowledge base for developers and researchers, organizing tools and frameworks by their primary programming language and technical domain to simplify discovery across the artificial intelligence ecosystem. The collection distinguishes itself by providing a cross-language development index that spans diverse programming environments, including C, C++, Rust, Clojure, and Python. It covers a wide range of specialized capabilities, fr

    Python
    View on GitHub↗72,867
Compare all 30 related projects→

Frequently asked questions

What does aaronwangy/data-science-cheatsheet do?

This project is a data science reference sheet and machine learning study guide. It provides a curated collection of formulas, definitions, and model summaries designed for quick lookup during project development and technical interview preparation.

What are the main features of aaronwangy/data-science-cheatsheet?

The main features of aaronwangy/data-science-cheatsheet are: Data Science Concepts, Technical Reference Sheets, Machine Learning Concepts, Interview Reviews, Machine Learning Algorithm Study Guides, Automated Knowledge Synthesis Tools, Curated Review Notes, Educational Topic Partitioning.

Which projects share features with aaronwangy/data-science-cheatsheet?

Projects with overlapping indexed features include: visualize-ml/book7_visualizations-for-machine-learning — This project is an educational collection of interactive Jupyter notebooks designed to illustrate fundamental machine… sreeharierk/datascience — This project is a curated knowledge base and learning resource for data science and artificial intelligence. It… jonkrohn/ml-foundations — ML-foundations is a machine learning educational curriculum and computer science study guide. It provides a structured… josephmisiti/awesome-machine-learning — This project is a comprehensive, community-driven directory of machine learning resources, software libraries, and… alexeygrigorev/data-science-interviews — This project is a curated knowledge repository providing theoretical guides, practical challenge banks, and… darliner/algorithm_interview_notes-chinese — This is a Chinese-language technical interview preparation resource focused on algorithms and data structures. It…