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r0f1/datascience

A curated Awesome-style list of Python resources for data science, covering libraries, tutorials, books, datasets and talks.

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What is it?

This is an awesome-style curated list of resources for practicing data science with Python. It organizes links by topic, from core libraries such as pandas, scikit-learn, matplotlib and seaborn, through general Python tooling like uv, ruff and just, to specialized areas including DataFrame alternatives, Jupyter workflows, OCR, big data, statistics and visualization.

Beyond software packages, the list also points to statistics books, p-value and effect size references, interactive statistics demos, R-based datasets, machine learning tutorials, and blog posts on feature engineering and dimensionality reduction. Many entries are annotated with a one-line note on what the item does or why it is useful.

The list is useful as a starting point when looking for a tool for a specific data task, such as cleaning messy columns, validating schemas, selecting features or building dashboards. Because most entries are external links, it works best as a discovery index rather than a single library to install.

Key features

  • 01Covers core Python data libraries such as pandas, scikit-learn, matplotlib and seaborn.
  • 02Lists DataFrame alternatives including polars, duckdb, modin and vaex.
  • 03Includes resources on Jupyter workflows, notebook tools and data dashboards.
  • 04Collects statistics references on p-values, effect sizes and statistical tests.
  • 05Groups feature selection, dimensionality reduction and data validation tools by topic.

Use cases

  • Finding a library for a specific data task such as imputation, feature selection or schema validation.
  • Learning statistics for data analysis through books, datasets and interactive demos.
  • Comparing pandas alternatives before choosing a DataFrame tool for a project.
  • Building a reading list for self-study in Python-based data science.

Topics

#data-science#python#awesome-list#machine-learning#statistics#data-analysis#data-visualization#pandas#artificial-intelligence#awesome

FAQ

What is datascience?
A curated Awesome-style list of Python resources for data science, covering libraries, tutorials, books, datasets and talks. This is an awesome-style curated list of resources for practicing data science with Python. It organizes links by topic, from core libraries such as pandas, scikit-learn, matplotlib and seaborn, through general Python tooling like uv, ruff and just, to specialized areas including DataFrame alternatives, Jupyter workflows, OCR, big data, statistics and visualization.
Is datascience free to use? What license does it have?
datascience is open source under the CC0-1.0 license. Check the license terms before using it in commercial products.
Is datascience still maintained?
Actively maintained. The last commit to datascience was on Oct 17, 2026.
What can I use datascience for?
Finding a library for a specific data task such as imputation, feature selection or schema validation. Learning statistics for data analysis through books, datasets and interactive demos. Comparing pandas alternatives before choosing a DataFrame tool for a project. Building a reading list for self-study in Python-based data science.
Where can I get datascience?
The source code and installation instructions are on GitHub: https://github.com/r0f1/datascience

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