A curated list of courses, books, tools, and resources for data science.

Microsoft's data science curriculum.

Open Source Society University's self-study path.

Carefully curated links for data science resources in one place.

A collection of articles, videos, and code related to data science.

Resources for data analysis using Python.

Structured learning path for analysts.

Comprehensive roadmap for data science.

A comprehensive and updated roadmap for learning data science with modern tools and technologies.

66-day data analytics learning challenge.

8-week program for analysts at all levels.

Tutorials for the data science roadmap.

Guide to becoming a data analyst from scratch.

An opinionated list of awesome Python frameworks, libraries, software, and resources.

A 30-day programming challenge to learn the Python programming language.

Tutorials on Python from Real Python.

A curated list of Python resources for data science.

Full text of the "Python Data Science Handbook" in Jupyter Notebooks.

120+ interactive Python coding interview challenges.

Clean Code concepts adapted for Python.

A ranked list of awesome Python open-source libraries and tools.

Python tutorial from GeeksforGeeks.

A beginner-friendly tutorial and reference for the Python programming language.

This repository helps you understand Python from scratch.

Jupyter notebooks and other resources for Think Python by Allen Downey.

A curated list of resources for using the Pandas library.

A collection of data puzzles to practice your Pandas skills.

Visualize Pandas operations step-by-step (perfect for beginners).

Exercises designed to help you improve your Pandas skills.

A cookbook with various recipes for using Pandas effectively.

Materials for following along with Hands-On Data Analysis with Pandas.

A series focused on writing effective and idiomatic Pandas code.

An open-access book on vectorization and efficient numerical computing with NumPy.

A collection of 100 exercises to master the NumPy library for scientific computing.

Powerful Python library for data analysis and manipulation with flexible data structures.

Fundamental package for scientific computing in Python with multidimensional array support.

Data type correction and automatic DataFrame cleaning.

High-performance Python library for lazy Out-of-Core DataFrames.

Multithreaded, vectorized query engine for DataFrames.

Unified interface for Pandas, Spark, and Dask.

Fuzzy string matching (Levenshtein distance).

Extensions for standard Python datetime features.

Enhanced work with dates and times.

Alternative to datetime with timezone support.

Parallel computing for arrays and DataFrames.

Speeds up Pandas by distributing computations.

Parallel operations for pandas DataFrames.

Python tool for automatically cleaning and preparing datasets.

Add custom methods to Pandas.

Reads data from various online sources into pandas DataFrames.

Bridge between Pandas and Scikit-learn.

A NumPy-compatible array library accelerated by NVIDIA CUDA for high-performance computing.

A JIT compiler that translates a subset of Python and NumPy code into fast machine code.

Type stubs for pandas, improves IDE autocompletion.

ETL tool for data cleaning and transformation.

Automatic data visualization in 1 line of code.

Automatic EDA with dataset comparison.

Automatic DataFrame visualization in Jupyter.

Data quality profiling & exploratory data analysis.

Visualize missing data patterns.

Low-code toolkit for building data visualization apps.

Visual diagnostic tools for machine learning.

Create awesome display tables using Python.

Create beautiful plots of data maps.

Quickly and accurately render even the largest data.

Conversational data analysis using LLMs and RAG.

Jupyter extensions for faster code writing.

Interactive GUI for data analysis in a browser.

GUI for viewing and filtering DataFrames.

Interactive UIs for visual analysis of DataFrames.

Interactive grid for DataFrames in Jupyter.

Interactive PivotTable.js tables in Jupyter.

Outlier and anomaly detection.

Outlier, adversarial and drift detection.

Data validation through declarative schemas.

Data validation through schemas.

Data validation using Python type annotations.

Automate EDA: preprocessing, feature engineering, visualization.

Data validation and testing.

Automated feature engineering.

Feature engineering with Scikit-Learn compatibility.

Multivariate exploratory data analysis (PCA, CA, MCA).

Figures out the distribution your data comes from.

Tool for dimensionality reduction of machine learning datasets.

Extensive collection of categorical variable encoders.

Handling imbalanced datasets.

A GPU DataFrame library for loading, joining, and aggregating data.

Generates fake data for testing.

Generates realistic test data.

Geocoding addresses and calculating distances.

Spatial analysis functions.

Beautiful visualizations of language differences among document types.

A library for creating and manipulating graphs and networks, with bindings for multiple languages.

A lightweight pipelining library for Python, particularly useful for saving and loading large NumPy arrays.

A library that provides an easy interface to read and write a wide range of image data.

Text preprocessing, representation and visualization.

Geographic data operations with pandas.

Network analysis and graph theory.

Python library to detect the character encoding of text and files.

Interactive SQL tutorial.

Learn SQL through interactive lessons.

Comprehensive SQL tutorial resource.

Comprehensive SQL tutorial.

Tutorial for PostgreSQL.

Tutorial for MySQL.

Tutorial for MongoDB.

AI-powered SQL query optimization and database observability tool.

Educational resources on database internals, distributed systems, and storage.

A curated list of awesome PostgreSQL software, libraries, tools and resources.

A curated list of awesome MySQL software, libraries, tools and resources.

A curated list of awesome ClickHouse software.

A curated list of awesome MongoDB resources, libraries, tools, and applications.

Curated tools, resources, and extensions for DuckDB analytical database.

A curated list of awesome tools for SQLAlchemy.

List of tools and techniques for working with relational databases.

Interactive tool that visualizes the step-by-step execution of SQL queries.

Useful SQL techniques and optimizations for data analysis.

Free interactive SQL tutorial site focused on mastering window functions through 80+ hands-on problems with hints and…

Embedded, file‑based SQL engine, the most used database in the world.

Advanced open‑source relational database with extensibility and SQL compliance.

Widely used relational database management system (Oracle’s community version).

Community developed fork of MySQL with enhanced features.

In‑process analytical database designed for fast OLAP queries.

Columnar database for real‑time analytics on large datasets.

A mature open-source MPP database evolved from Greenplum with a newer PostgreSQL kernel.

Time‑series SQL database built on PostgreSQL.

Time‑series database for IoT, vehicles, and industrial monitoring.

Document database with a flexible, JSON‑like data model.

Highly scalable, distributed NoSQL database for large‑scale deployments.

In‑memory data structure store used as database, cache, and message broker.

Distributed SQL database with strong consistency and horizontal scaling.

Native graph database for connected data and relationships.

Purpose‑built time‑series database for metrics and events.

Drop‑in Cassandra alternative written in C++ for higher performance.

Distributed, scalable, big data store modeled after Google's Bigtable, running on top of HDFS.

Python library for ODBC database access.

SQL toolkit and ORM for Python.

PostgreSQL database adapter.

MySQL driver for Python.

ORM for Python with dynamic query generation.

Official MongoDB driver for Python.

A tool for exploring SQLite databases and visualizing the results of your queries.

A high quality, visual, open source tool to create, design, and edit database files compatible with SQLite.

A free universal database tool and SQL client for developers, SQL programmers, and administrators.

A modern, easy-to-use SQL client and database manager with a clean, cross-platform interface.

Database IDE for exploring, migrating, and syncing PostgreSQL, MySQL, files, and S3.

A modular SQL linter and auto-formatter designed to enforce consistent style and catch errors in SQL code.

A pure-Python MySQL client library for interacting with MySQL databases from Python applications.

An AI-powered tool for generating SQL queries from natural language questions.

A chat-based SQL client that allows you to query databases using natural language conversations.

SQL queries to databases via Python syntax.

JSON-like interface for working with SQL databases.

A no-dependency SQL parser, transpiler, and optimizer for Python.

A guide to choosing the right visualization based on your data.

A curated list of awesome data visualization libraries, tools, and resources.

Interactive notebooks designed to teach data visualization concepts.

Guide to creating effective scientific visualizations and plots.

A collection of Python graph examples for data visualization.

Insights on data analysis and visualization.

A comprehensive catalog of data visualization types.

A resource for selecting suitable visualizations.

A guide to help you select the appropriate chart types.

Tutorials and insights on data visualization techniques.

Exploration of the aesthetics of data visualization.

A collection of top data visualization resources and inspiration.

A site dedicated to visualizations that make complex ideas clear and engaging.

A vast library of scientific plots for visualization inspiration and ideas.

A collection of bad data visualization practices and better alternatives.

A digital archive of historical color systems and pigments.

Resource for generating categorical color palettes using perceptual principles.

A comprehensive library for creating static, animated, and interactive visualizations in Python.

A statistical data visualization library based on Matplotlib.

A library for creating interactive plots and dashboards.

A declarative statistical visualization library for Python.

A library for creating interactive visualizations for modern web browsers.

A tool for building complex visualizations easily.

An extension of Pandas for geospatial data.

A library for visualizing data on interactive maps.

A Python SVG charting library.

A grammar of graphics for Python.

A plotting library for IPython/Jupyter notebooks.

A large (+2500) collection of color maps for Python.

A WebGL-powered framework for visual exploratory data analysis of large datasets.

Contextily: add background basemaps to your plots in GeoPandas.

A package to easily download, model, analyze, and visualize street networks from OpenStreetMap.

A powerful, interactive charting and visualization library for browser-based applications.

A high-performance interactive 2D/3D data visualization library leveraging the power of OpenGL.

A Python library for scientific visualization that is fast, scalable and beautiful, based on OpenGL.

Bokeh plotting backend for Pandas.

Free, open source, cross-platform geographic information system (GIS).

Platform for creating interactive data visualizations and stories without coding.

A collection of outstanding dashboard and visualization resources.

Showcase of community-built Streamlit applications.

Comprehensive resources for Dash users.

Resources and support for Panel users.

Curated list of Streamlit resources and components.

Production-ready Dash apps.

Comprehensive tutorial on Tableau.

Detailed tutorial on Power BI.

A curated collection of real-world interactive dashboards to inspire and learn from.

Framework for creating interactive web applications.

Simplified framework for building data applications.

Python library for creating custom interactive web apps and dashboards.

Tool for creating and sharing machine learning applications.

A powerful data visualization and dashboarding tool for OpenSearch data, forked from Kibana.

A library for building draggable, resizable responsive dashboard layouts.

A React library to build dashboards fast with pre-built components for charts, KPIs, and more.

An open-source platform to build and deploy internal tools, admin panels, and CRUD apps quickly.

A Python library for generating Grafana dashboards configuration as code.

A Python framework for rapidly building and deploying realtime web apps and dashboards for AI and analytics.

Python version of the popular R Shiny framework.

Turn Jupyter notebooks into standalone web applications.

Full-stack Python framework for building web apps.

Python library for building web applications and interactive dashboards.

Business intelligence platform that uses SQL and Markdown for reports.

Leading data visualization software.

Business analytics tool for visualizing data.

Tool for data visualization and business intelligence.

User-friendly open-source BI tool.

Open-source data exploration and visualization platform.

A platform for modern business intelligence, providing a hosted version of Apache Superset.

The simplest way to get analytics and business intelligence for everyone in your company.

Tool for visualizing and sharing data insights.

Dashboarding and monitoring tool.

User-friendly chart and map creation tool.

Online chart creation platform.

Tool for creating infographics and visual content.

Free tool for creating interactive dashboards and reports.

Next-generation automated data exploratory analysis and visualization platform.

The official visualization and dashboarding tool for the Elastic Stack (Elasticsearch, Logstash, Beats).

List of libraries, tools, and APIs for web scraping and data processing.

Code samples from the book "Web Scraping with Python".

Tutorial for scraping streaming sites.

An open project repository sharing knowledge and experiences about web scraping with Python.

A simple, yet elegant, HTTP library for Python.

A library for parsing HTML and XML documents.

A tool for automating web applications for testing purposes.

An open-source and collaborative web crawling framework for Python.

A library for browser automation and web scraping.

Distributed Crawler Management Framework based on Scrapy, Scrapyd, Django, and Vue.js.

A smart, automatic, fast, and lightweight web scraper for Python.

A library to parse feeds in Python.

A Python & command-line tool to gather text and metadata on the web.

A tiny command-line utility to download media contents (videos, audios, images) from the web.

A Python library for automating interaction with websites.

A Python scraper based on AI.

A social networking service scraper in Python.

A web scraping system that lets you declaratively describe what data to extract using a simple query language.

A Python framework for building web scraping apps, providing a high-level API for asynchronous requests.

Python version of the Playwright browser automation library.

A jQuery-like library for parsing HTML documents in Python.

High-level Selenium wrapper for easier web automation.

A framework for building web scrapers and crawlers.

Advanced web crawling framework designed for AI and data extraction tasks.

A curated list of mathematics resources, books, and online courses.

Comprehensive resource for mathematics in machine learning.

Visual explanations of mathematical concepts through animated videos.

Interactive resource for understanding linear algebra.

Resource for learning statistics and mathematics for data science.

Collection of Python scripts and notebooks for statistics and mathematics.

Resource for learning linear algebra computationally.

A curated list of statistics resources, software, and learning materials.

Notebooks for understanding statistical learning concepts.

Interactive visual resource for learning probability and statistics.

Companion code for a practical statistics book.

Lecture notes on statistical learning theory.

Comprehensive online textbook covering probability and statistics concepts.

Refresher course on probabilities and statistics from Stanford University.

Resource for learning Bayesian methods in Python.

Code for the book "Bayesian Modeling and Computation in Python".

A resource for learning statistics and probability, with tutorials and tools.

An interactive online statistics book with simulations and demonstrations.

Resource for studying statistics based on Wasserman's book.

Book and code for an introduction to Probability and Statistics.

Book and code for Bayesian statistical methods.

Practical guide to causal inference methods.

Modern introduction to causality and research design.

Open-source statistics hands-on handbook.

Fundamental library for scientific computing and statistics.

Statistical modeling, testing, and data exploration.

A probabilistic programming library for Python that allows for flexible Bayesian modeling.

Statistical package with improved usability over SciPy.

Post-hoc tests for statistical analysis of data.

Survival analysis and event history analysis in Python.

Survival analysis built on scikit-learn for time-to-event prediction.

Bootstrap confidence interval estimation methods.

Python interface to Stan for Bayesian statistical modeling.

Exploratory analysis of Bayesian models with visual diagnostics.

A Python library for generalized additive models with built-in smoothing and regularization.

A probabilistic programming library built on JAX for high-performance Bayesian modeling.

A Python implementation of the R package for causal inference using Bayesian structural time-series models.

A Python library for causal inference that supports explicit modeling and testing of causal assumptions.

A Python library for describing statistical models and building design matrices.

Fast and flexible probabilistic modeling library for Python with GPU support.

Python library for probabilistic and causal inference using graphical models.

An online course covering advanced testing and optimization techniques.

A/B test calculators.

A practical guide to A/B testing and experimentation from industry leaders.

A free Udacity course covering the fundamentals of A/B testing.

Experience the challenges of A/B testing through this educational simulation.

A curated list of resources dedicated to time series analysis and forecasting.

Comprehensive textbook on forecasting methods with practical examples.

Official time series analysis guide from NIST.

A curated list of tools, datasets, and papers dedicated to time series anomaly detection.

A comprehensive list of Python tools and libraries for time series analysis.

A procedure for forecasting time series data based on an additive model.

A Python package for Bayesian time series forecasting and inference.

A unified Python framework for machine learning with time series, compatible with scikit-learn.

A Python toolkit for probabilistic time series modeling, built on MXNet.

A library for deep learning-based time series analysis and forecasting.

A pretrained time series foundation model from Google Research for zero-shot forecasting.

A PyTorch-based library for time series forecasting with neural networks.

A collection of time series prediction methods and implementations.

A tool to visualize and analyze time series data logs in real-time.

Automatically extracting features from time series data.

Python library for ARIMA modeling and time series analysis.

Toolkit for analyzing time series data from Facebook Research.

A comprehensive guide covering fundamental and advanced data engineering concepts.

Free course on data engineering fundamentals.

A curated list of data engineering tools, software, and resources.

Techniques and strategies for building reliable data platforms.

A curated list of pipeline toolkits for data processing and workflow management.

A curated list of awesome database tools.

Curated resources for learning and working with Apache Kafka: books, trainings, tools.

A framework for transforming data in your warehouse using SQL and Jinja.

A unified engine for large-scale data processing and analytics.

A distributed event streaming platform for building real-time data pipelines.

A data orchestrator for machine learning, analytics, and ETL.

A platform to programmatically author, schedule, and monitor workflows.

A data warehouse software for reading, writing, and managing large datasets in distributed storage using SQL.

A framework that allows for the distributed processing of large data sets across clusters of computers.

A Python module for building complex and batch-oriented data pipelines.

A high-performance table format for huge analytic datasets.

Highly scalable, distributed NoSQL database for large‑scale deployments.

A framework for stateful computations over unbounded and bounded data streams (real-time stream processing).

A unified model for defining both batch and streaming data-parallel processing pipelines.

A cloud-native, distributed messaging and streaming platform.

A storage layer that brings ACID transactions to Apache Spark and big data workloads.

An open data lakehouse platform, built on a high-performance open table format.

A distributed SQL query engine designed for fast analytic queries against large datasets.

A metadata platform for the modern data stack.

An open framework for collection and analysis of data lineage.

A framework for creating reproducible, maintainable and modular data science code.

A dynamic data management framework that allows for SQL parsing, optimization, and federation.

Workflow orchestration for building resilient data pipelines.

Universal columnar format and multi-language toolbox for fast data interchange.

An open-source, event-driven orchestrator that simplifies data workflow management.

Orchestration engine for running complex, multi-step workflows and business processes.

A ranked list of awesome Python libraries for natural language processing (NLP).

Official course on transformers and NLP from Hugging Face.

Code examples and notebooks for practical natural language processing.

Lecture materials from Oxford's Deep Natural Language Processing course.

Natural Language Processing with Python.

Jupyter notebooks demonstrating various NLP techniques and applications.

The hands-on NLTK tutorial for NLP in Python.

Yandex School of Data Analysis course on Natural Language Processing.

Comprehensive NLP guide covering theory, models, and practical implementations.

A leading platform for building Python programs to work with human language data.

A simple library for processing textual data.

An open-source software library for advanced NLP in Python.

A transformer-based model for NLP tasks.

A simple framework for state-of-the-art NLP.

A library and framework for building applications with large language models.

A Java suite of core NLP tools providing fundamental linguistic analysis capabilities.

A state-of-the-art Natural Language Processing library built on Apache Spark.

A Python framework for adversarial attacks, data augmentation, and model training in NLP.

Topic modeling and natural language processing library for Python.

Python NLP library for many human languages, from the Stanford NLP Group.

Framework for state-of-the-art sentence and text embeddings.

Google's library for structured information extraction from text using language models.

Open-source framework for building contextual AI assistants and chatbots.

A curated list of awesome Machine Learning frameworks, libraries and software.

Machine learning and deep learning tutorials, articles and other resources.

A curated list of awesome Deep Learning tutorials, projects and communities.

A ranked list of awesome machine learning Python libraries and tools.

A beginner-friendly introduction to machine learning concepts and practices.

Open Machine Learning Course with practical assignments and real-world applications.

A free practical machine learning course focused on building and deploying models.

A curated list of artificial intelligence resources.

Official repository for Google Research projects and publications.

A comprehensive coding challenge to learn machine learning over 100 days.

Resource for building and deploying machine learning applications.

Hands-on guide to machine learning and deep learning using Python.

Microsoft's curriculum on artificial intelligence.

Educational repository for building LLMs from scratch.

A comprehensive guide to generative AI models, tools, and applications.

A curated list of papers, projects, and resources related to Large Language Models.

Jupyter notebooks covering various machine learning algorithms and applications.

Comprehensive and accessible textbook on deep learning fundamentals.

Curated roadmap of seminal deep learning papers for newcomers.

Curated resources and tools for applied machine learning in industry.

Implementations of deep learning papers with annotated code.

Core machine learning algorithms implemented in Python from scratch.

Carefully curated list of AI/ML books, courses, and practical tools.

Machine learning library for classical algorithms and model building.

Optimized distributed gradient boosting library for tree-based models.

Fast, distributed, high-performance gradient boosting framework.

High-performance gradient boosting on decision trees with categorical features support.

Open-source distributed machine learning platform.

GPU-accelerated machine learning algorithms from RAPIDS.

Modern C++ toolkit containing machine learning algorithms and tools.

Game theoretic approach to explain the output of any machine learning model.

Fit interpretable models and explain blackbox machine learning.

Hyperparameter optimization framework.

End-to-end open source platform for machine learning and deep learning.

Deep learning framework with strong support for research and production.

PyTorch wrapper for high-performance AI research.

High-level library to help with training and evaluating neural networks.

High-level neural networks API, running on top of TensorFlow.

Deep learning library simplifying training fast and accurate neural nets.

Model-definition framework for state-of-the-art machine learning models.

Library for state-of-the-art pretrained diffusion models.

Library for efficiently adapting large pretrained models.

Real-time object detection system.

YOLOv8 and other computer vision models.

Open standard for machine learning interoperability.

Geometric deep learning extension library for PyTorch.

Deep universal probabilistic programming with Python and PyTorch.

Scikit-learn compatible neural network library.

DeepMind's library for building complex neural networks.

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.

Official TensorFlow repository with models and examples.

A simple framework that automates ML/DL workflows by providing prebuilt trainers, templates, logging, configuration…

A free course focused on the practical aspects of deploying and maintaining ML systems.

A curated list of references for MLOps.

A curated list of awesome MLOps tools.

An awesome & curated list of best LLMOps tools for developers.

A course dedicated to Large Language Models, their architecture and applications.

A practical guide to machine learning engineering and MLOps best practices.

A curated list of tools for deploying, monitoring, and maintaining ML systems in production.

Official recipes and examples for working with Llama models.

Curated resources, tools, and projects for the Kubeflow machine learning platform.

High-performance distributed training framework.

Version control system for machine learning projects.

Tool for analyzing and monitoring data and model drift.

Validation for ML models and data.

Tool to build, debug, and execute ML pipelines with native Python.

Real-time performance monitoring.

Fast, open-source search engine.

High-throughput and memory-efficient inference library for LLMs.

LLM framework for building search and question answering systems.

Machine learning toolkit for Kubernetes.

Open source platform for deploying and monitoring machine learning models in production.

A feature store for machine learning that manages and serves ML features to models.

Framework for building, shipping, and scaling ML applications.

Open-source platform for the complete machine learning lifecycle.

Tool for experiment tracking, dataset versioning, and model management.

ML platform for tracking, comparing and optimizing experiments.

A human-friendly Python library for helping scientists and engineers build and manage real-life data science projects.

Platform for integrating AI into databases and applications.

Standardized serverless inference platform for deploying and serving machine learning models on Kubernetes.

Brings machine learning capabilities to SQL, enabling model training and prediction using SQL syntax.

Framework for building and deploying AI services that communicate via gRPC, HTTP and WebSockets.

Unified interface to call all LLM APIs (OpenAI, Anthropic, Cohere, etc.) with consistent output formatting.

Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.

A curated list of modern Generative Artificial Intelligence projects and services.

Microsoft's course on designing and building AI agents.

Course on generative AI for beginners from Microsoft.

Free hands-on course on modern tools for building and deploying AI applications.

Practical course to master large language models from start to finish.

A curated list of AI autonomous agents, environments, and frameworks.

The Generative AI Landscape - A Collection of Awesome Generative AI Applications.

A collection of projects showcasing RAG, agents, workflows, and other AI use cases.

System Prompts, Internal Tools & AI Models from various AI applications and coding tools.

Collection of advanced techniques for Retrieval-Augmented Generation.

Awesome list of tools and projects with the awesome LangChain framework.

A curated list of Artificial Intelligence Top Tools.

A curation of awesome tools, documents and projects about LLM Security.

Official Anthropic examples and recipes for working with Claude AI.

Covers LLM fundamentals, prompt engineering, and fine-tuning.

Resources for building, deploying, and maintaining AI systems.

Code-first tutorials for building production-grade GenAI agents.