Open Source Data Science Projects
Data science combines statistical methods, computing, and subject-matter knowledge to extract useful insights from data. It helps people organize and explore datasets, identify patterns, make predictions, and communicate findings. Work in this area can support decisions across research, business, public services, and many other fields, from initial data preparation through analysis and deployment.
24 repositories · updated October 3, 2026
AREX-Skill: A Skill Library for Automated Machine Learning and Auto-Research
AREX-Skill is a powerful skill library designed to advance automated machine learning and auto-research. It distills over 5,000 executable skills from more than 1,000 popular GitHub repositories, making complex ML knowledge directly usable by coding agents. This project significantly enhances agent performance in various research tasks by providing structured, validated operating knowledge.
Awesome Automated AI/ML: Your Curated Guide to AI/ML Automation Tools
Awesome Automated AI/ML is a comprehensive, curated list featuring over 300 tools for automating various aspects of AI and Machine Learning. It covers everything from hyperparameter optimization to autonomous AI agents, offering a dynamic resource for ML engineers, AI researchers, and product builders.
Free-OReilly-Books: Access a Curated Collection of Free O'Reilly Books
This GitHub repository, `Free-OReilly-Books` by mohnkhan, offers a valuable collection of free O'Reilly books across various technical and business domains. It serves as an excellent resource for developers, data scientists, IoT enthusiasts, and business professionals looking to expand their knowledge with high-quality content. With over 800 stars, it's a popular choice for accessing free educational materials.
ds-cheatsheets: Your Ultimate Collection of Data Science Cheatsheets
The ds-cheatsheets repository by FavioVazquez offers an extensive collection of quick reference guides for data science. It covers a broad spectrum of topics, including programming languages like Python and R, and advanced concepts in Machine Learning and Deep Learning. This resource is perfect for anyone needing a handy guide to navigate the complex world of data science.
awesome-R: A Curated List of Essential R Packages and Tools
awesome-R is a highly popular GitHub repository, maintained by qinwf, offering a meticulously curated list of R packages, frameworks, and software. It serves as an invaluable resource for anyone working with R, from data analysis to machine learning. With over 6,400 stars, it stands as a testament to its utility and community recognition.
Evidently: Open-Source ML and LLM Observability Framework
Evidently is an open-source Python library designed for evaluating, testing, and monitoring machine learning and large language model systems. It provides over 100 built-in metrics for various tasks, from data drift detection to LLM judges, supporting both tabular and text data. This framework helps ensure the quality and performance of AI-powered systems throughout their lifecycle.
physicsnemo: Build and Train Physics AI Models
NVIDIA PhysicsNeMo is a PyTorch framework for building and training machine-learning models for physics and engineering. It combines reusable model components with end-to-end recipes for scientific data and workloads.
TensorRec: A TensorFlow Recommendation Framework in Python
TensorRec is a Python recommendation system built on TensorFlow, designed for quickly developing and customizing recommendation algorithms. It allows users to define custom representation and loss functions while handling data manipulation, scoring, and ranking. Although not under active development, it provides a solid foundation for understanding and implementing recommender systems.
Vizro: Low-Code Toolkit for High-Quality Data Visualization Apps
Vizro is an open-source, Python-based toolkit designed for building high-quality data visualization applications with a low-code approach. It enables users to create beautiful, powerful, and production-ready dashboards quickly, leveraging trusted dependencies like Plotly, Dash, and Pydantic. The toolkit offers flexibility, customization options, and scalability for various data visualization needs.
fastFM: A High-Performance Python Library for Factorization Machines
fastFM is a powerful Python library designed for Factorization Machines, offering high-performance implementations of various optimization routines. It integrates seamlessly with the scikit-learn API, making it accessible for machine learning practitioners. The library supports regression, classification, and ranking problems, leveraging C and Cython for speed-critical operations.
ML-From-Scratch: Machine Learning Models and Algorithms in NumPy
ML-From-Scratch is a comprehensive GitHub repository offering bare-bones NumPy implementations of fundamental machine learning models and algorithms. It emphasizes accessibility, making complex concepts easier to understand for learners and practitioners. This project covers a wide range of topics, from linear regression to deep learning and reinforcement learning, all implemented from scratch.
Rio: Build Web and Desktop Apps in Pure Python, No JavaScript Needed
Rio is an innovative Python framework that allows developers to create web and desktop applications using pure Python, eliminating the need for HTML, CSS, or JavaScript. It provides a modern, declarative UI approach with over 50 built-in components, making app development efficient and enjoyable. With Rio, you can build powerful, type-safe applications that run seamlessly across different environments.