Data Science
Showing 36 of 624 projects
A curated guide to learning machine learning with Python and Jupyter Notebook, featuring courses, notebooks, and practical resources.
A curated guide to learning machine learning with Python and Jupyter Notebook, featuring hands-on tutorials, courses, and ethical considerations.
A Python framework for creating reproducible, maintainable, and modular data engineering and data science pipelines.
An automated machine learning library that trains and deploys high-accuracy models for tabular, text, image, and time series data with minimal code.
A practical booklet covering the four main steps of designing machine learning systems with 27 interview questions.
A declarative statistical visualization library for Python built on Vega-Lite.
A drop-in replacement for pandas that scales data analysis workflows to use all CPU cores and handle out-of-memory datasets.
A community-driven collection of data science interview questions and answers covering theory, technical skills, and probability.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A standardized, flexible project template for data science work using Cookiecutter to structure reproducible projects.
A Python library for anomaly detection across tabular, time series, graph, text, image, and audio data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.
A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
An open-source, low-code Python library that automates end-to-end machine learning workflows.
A GPU-accelerated DataFrame library for tabular data processing, part of the RAPIDS data science suite.
A modular deep learning library providing a higher-level API for TensorFlow to speed up experimentation.
A Python library for user-friendly forecasting and anomaly detection on time series, from ARIMA to deep neural networks.
A batteries-included machine learning library for Go with a scikit-learn inspired interface.
Automatically extracts and selects relevant features from time series data for machine learning tasks.
A curated list of practical financial machine learning tools, applications, and research repositories.
A high-performance Python DataFrame library for lazy out-of-core processing and visualization of billion-row datasets at interactive speeds.
A collection of ready-to-run Docker images containing Jupyter applications and interactive computing tools.
A collection of ready-to-run Docker images containing Jupyter applications and interactive computing tools.
A multi-user server that spawns, manages, and proxies multiple instances of single-user Jupyter notebook servers.
A weekly social data project providing real-world datasets for practicing data tidying, visualization, and analysis.
Code and Jupyter notebooks for the book 'Introduction to Machine Learning with Python' by Andreas Mueller and Sarah Guido.
A comprehensive cheat sheet with classical equations and diagrams for machine learning knowledge recall and interview preparation.
An open-source Python framework to evaluate, test, and monitor ML and LLM systems with 100+ built-in metrics.
An open-source Python library for automated feature engineering using Deep Feature Synthesis.
An open-source Python library for automated feature engineering using Deep Feature Synthesis.
An open-source, in-memory platform for distributed and scalable machine learning with support for a wide range of algorithms and big data technologies.
A cross-platform, language-agnostic binary package and environment manager for creating isolated software environments.
Python Data. Leaflet.js Maps.
A curated collection of tutorials and resources following the data scientist roadmap for learning essential data science skills.
Convert Jupyter notebooks to and from plain text formats like Markdown, Python, Julia, or R scripts for better version control and editing.
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