awesome-datascience and awesome-python-data-science

These are complements: the first is a broad, language-agnostic data science resource collection, while the second is a specialized curated list focused specifically on Python data science tools, allowing users to reference both for general guidance and Python-specific implementations.

Maintenance 23/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 13/25
Adoption 10/25
Maturity 16/25
Community 21/25
Stars: 28,556
Forks: 6,397
Downloads:
Commits (30d): 39
Language:
License: MIT
Stars: 3,373
Forks: 430
Downloads:
Commits (30d): 3
Language:
License: CC-BY-4.0
No Package No Dependents
No Package No Dependents

About awesome-datascience

academic/awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

Curated collection of ML algorithms, deep learning frameworks (PyTorch, TensorFlow, Keras), and evaluation tools organized by learning approach—supervised, unsupervised, reinforcement, and semi-supervised learning. Bridges theory and practice by combining foundational resources, datasets, and competitions with practical tooling for model evaluation and monitoring. Targets learners across all levels through structured curriculum paths, peer communities (Slack, Telegram, GitHub groups), and curated literature spanning books, journals, podcasts, and YouTube channels.

About awesome-python-data-science

krzjoa/awesome-python-data-science

Probably the best curated list of data science software in Python.

Scores updated daily from GitHub, PyPI, and npm data. How scores work