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Data Science Repositories & Open Source Data Projects

Explore the most popular data science repositories, analytics tools, and open source data projects. From machine learning workflows and data analysis libraries to notebooks, visualization tooling, and applied data science frameworks, discover which projects are gaining traction on GitHub.

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Trending Data Repositories

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  1. #1King of the HillDataPython

    RepoRank Score

    69

    scrapy/scrapy

    scrapyscrapy

    Scrapy, a fast high-level web crawling & scraping framework for Python.

    —61,317GitHub stars0boosts (24h)+14stars (24h)

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Explore Open Source Data Science

Data science sits at the intersection of analysis, experimentation, modeling, and real-world decision-making. Open source repositories play a major role in this ecosystem, giving developers, analysts, and researchers access to practical workflows for data cleaning, exploration, machine learning, visualization, and reproducible analysis.

The open source data science landscape includes notebooks, analysis frameworks, machine learning libraries, visualization tools, data workflow projects, and applied repositories built around real datasets and practical problem-solving. RepoRank helps surface the repositories that are earning real attention and momentum.

What You Will Find Here

  • Data science libraries, analysis workflows, and notebook projects
  • Machine learning, data visualization, and experimentation tools
  • Applied data repositories and reproducible analytics workflows
  • Emerging data science projects gaining traction

This page helps you discover the data science tools and repositories developers, analysts, and technical teams are actively using, evaluating, and watching.

Why RepoRank Is Different

RepoRank focuses on real GitHub growth signals, helping you identify data science repositories that are active, relevant, and gaining adoption across analysis and machine learning workflows.

  • Live GitHub star growth and activity tracking
  • A mix of established data tooling and rising projects
  • A discovery layer built for practical data science work

Built for Analysts, Engineers, and Data Teams

Whether you are exploring machine learning workflows, evaluating analysis libraries, or tracking open source data projects gaining traction, this page helps you stay close to the repositories shaping modern data science.

  • Data scientists and analysts working with modern toolchains
  • Engineers evaluating data workflows and ML foundations
  • Teams tracking fast-moving open source data projects

Use this page to discover trending data science repositories, compare tools, and stay current with the open source projects shaping modern analytics and machine learning workflows.

Data Science FAQ

What are data science repositories?

Data science repositories are open source codebases related to data analysis, machine learning, visualization, experimentation, notebooks, and broader workflows for working with data.

What types of data science projects are included here?

This page includes analysis libraries, machine learning tools, notebook-driven workflows, visualization projects, reproducible data pipelines, and broader open source data science repositories.

How does RepoRank rank data science repositories?

RepoRank uses real GitHub growth signals such as star growth, activity, and project momentum to surface data science projects that are gaining traction.

Are these data science repositories open source?

Yes, all featured repositories are open source projects sourced directly from GitHub.

Why should I track trending data science repositories?

Tracking trending data science repositories helps you discover new workflows, stay current with analysis and machine learning tooling, and evaluate the projects technical teams are actively adopting.

Are data science repositories only for researchers?

No. Data science repositories are also useful for analysts, product teams, engineers, startup founders, and developers building practical data-driven applications and workflows.

What is the difference between data science and data engineering tools?

Data science tools are often focused on analysis, modeling, experimentation, and insight generation, while data engineering tools tend to focus more on data movement, storage, orchestration, and infrastructure at scale.

How do I choose the right data science repository?

Start with your workflow and goals. Consider whether you need analysis tooling, model experimentation, visualization, or applied examples, then evaluate documentation, ecosystem support, maintainability, and real-world usefulness.