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More Awesome Lists","🐍 Python","☁️ Cloud Platforms \u0026 Infrastructure","📈 Dashboards \u0026 BI","🏆 Awesome Data Science Repositories","🤖 Machine Learning \u0026 AI","🌐 Additional Resources and Tools","🧠 AI Applications \u0026 Platforms","📚 Skill Development \u0026 Career","⚙️ Data Engineering","🔢 Mathematics","📋 Cheatsheets","🗃️ SQL \u0026 Databases","📜 License","🕸️ Web Scraping \u0026 Crawling","📖 Natural Language Processing (NLP)","📦 Additional Python Libraries","📊 Data Visualization","⏳ Time Series Analysis","⚡ Productivity","🎲 Statistics \u0026 Probability","🔢 Mathematics, Statistics \u0026 Probability","🧪 A/B Testing","🚀 MLOps","🏗️ System Design \u0026 Architecture","🗺️ Roadmaps","📈 Dashboards","🤝 Contributing"],"sub_categories":["Miscellaneous","Resources","Data Sources \u0026 Datasets","Tools","Linux \u0026 Git","Documentation \u0026 File Processing","Python","Useful Linux Tools","Useful VS Code Extensions","Practice Resources","Software","Mathematics","Resume and Interview Tips","Useful Python Tools for Data Analysis","Data Science \u0026 Machine Learning","GoalKicker Programming Notes","SQL \u0026 Databases","Data Manipulation with Pandas and Numpy","Web \u0026 APIs","Code Quality \u0026 Development","Databases","Curated Jupyter Notebooks","Probability \u0026 Statistics"],"readme":"# Awesome Data Analysis [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n\n[![Web Page](https://img.shields.io/badge/🌐_Web_Page-696969)](https://pavelgrigoryevds.github.io/awesome-data-analysis/)\n[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](http://makeapullrequest.com)\n[![CC0](https://img.shields.io/badge/License-CC0_1.0-lightgrey.svg)](https://creativecommons.org/publicdomain/zero/1.0/)\n\n500+ curated resources for data analysis and data science: tools, libraries, roadmaps, cheatsheets, interview guides and more.\n\n**📖 For comfortable reading:** [Web version](https://pavelgrigoryevds.github.io/awesome-data-analysis/)\n\n**🌱 Want to improve?** [Suggest here](https://github.com/PavelGrigoryevDS/awesome-data-analysis/issues/16) or [Welcome to Discussions](https://github.com/PavelGrigoryevDS/awesome-data-analysis/discussions)\n\n🌟 Join us in making data analysis more efficient! ![GitHub stars](https://img.shields.io/github/stars/PavelGrigoryevDS/awesome-data-analysis?style=social)\n\nMaintained with ❤️\n\n---\n\n\u003ca id=\"contents\"\u003e\u003c/a\u003e\n\n## 📑 Contents\n\n- [🏆 Awesome Data Science Repositories](#awesome-data-science-repositories)\n- [🗺️ Roadmaps](#roadmaps)\n- [🐍 Python](#python)\n  - [Resources](#python-resources)\n  - [Data Manipulation with Pandas and Numpy](#python-data-manipulation-with-pandas-and-numpy)\n  - [Useful Python Tools for Data Analysis](#python-useful-python-tools-for-data-analysis)\n    - [Data Processing \\\u0026 Transformation](#python-data-processing-transformation)\n    - [Automated EDA and Visualization Tools](#python-automated-data-visualization-tools)\n    - [Data Quality \\\u0026 Validation](#python-data-quality-validation)\n    - [Feature Engineering \u0026 Selection](#python-feature-engineering-selection)\n    - [Specialized Data Tools](#python-specialized-data-tools)\n- [🗃️ SQL \\\u0026 Databases](#sql-databases)\n  - [Resources](#sql-databases-resources)\n  - [Databases](#sql-databases-engines)\n  - [Tools](#sql-databases-tools)\n- [📊 Data Visualization](#data-visualization)\n  - [Resources](#data-visualization-resources)\n  - [Tools](#data-visualization-tools)\n- [📈 Dashboards \u0026 BI](#dashboards)\n  - [Resources](#dashboards-resources)\n  - [Tools](#dashboards-tools)\n  - [Software](#dashboards-software)\n- [🕸️ Web Scraping \\\u0026 Crawling](#web-scraping-crawling)\n  - [Resources](#web-scraping-crawling-resources)\n  - [Tools](#web-scraping-crawling-tools)\n- [🔢 Mathematics](#mathematics)\n- [🎲 Statistics \\\u0026 Probability](#statistics-probability)\n  - [Resources](#statistics-probability-resources)\n  - [Tools](#statistics-probability-tools)\n- [🧪 A/B Testing](#ab-testing)\n- [⏳ Time Series Analysis](#time-series-analysis)\n  - [Resources](#time-series-analysis-resources)\n  - [Tools](#time-series-analysis-tools)\n- [⚙️ Data Engineering](#data-engineering)\n  - [Resources](#data-engineering-resources)\n  - [Tools](#data-engineering-tools)\n- [📖 Natural Language Processing (NLP)](#natural-language-processing-nlp)\n  - [Resources](#natural-language-processing-nlp-resources)\n  - [Tools](#natural-language-processing-nlp-tools)\n- [🤖 Machine Learning \u0026 AI](#machine-learning)\n  - [Resources](#machine-learning-resources)\n  - [Tools](#machine-learning-tools)\n- [🚀 MLOps](#mlops)\n  - [Resources](#mlops-resources)\n  - [Tools](#mlops-tools)\n- [🧠 AI Applications \u0026 Platforms](#ai-applications)\n  - [Resources](#ai-applications-resources)\n  - [Tools](#ai-applications-tools)\n- [☁️ Cloud Platforms \u0026 Infrastructure](#cloud-platforms)\n  - [Resources](#cloud-platform-resources)\n  - [Tools](#cloud-platform-tools)\n- [🏗️ System Design \u0026 Architecture](#system-design)\n- [⚡ Productivity](#productivity)\n  - [Resources](#productivity-resources)\n  - [Useful Linux Tools](#productivity-useful-linux-tools)\n  - [Useful VS Code Extensions](#productivity-useful-vs-code-extensions)\n- [📚 Skill Development \\\u0026 Career](#skill-development-career-resources)\n  - [Practice Resources](#skill-development-career-resources-practice-resources)\n  - [Curated Jupyter Notebooks](#skill-development-career-resources-curated-jupyter-notebooks)\n  - [Data Sources \\\u0026 Datasets](#skill-development-career-resources-data-sources-datasets)\n  - [Resume and Interview Tips](#skill-development-career-resources-resume-and-interview-tips)\n- [📋 Cheatsheets](#cheatsheets)\n  - [GoalKicker Programming Notes](#cheatsheets-goalkicker)\n  - [Python](#cheatsheets-python)\n  - [Data Science \\\u0026 Machine Learning](#cheatsheets-data-science-machine-learning)\n  - [Linux \\\u0026 Git](#cheatsheets-linux-git)\n  - [Probability \\\u0026 Statistics](#cheatsheets-probability-statistics)\n  - [SQL \\\u0026 Databases](#cheatsheets-sql-databases)\n  - [Miscellaneous](#cheatsheets-miscellaneous)\n- [📦 Additional Python Libraries](#additional-python-libraries)\n- [📝 More Awesome Lists](#more-awesome-curations)\n- [🌐 Additional Resources and Tools](#additional-resources)\n- [🤝 Contributing](#contributing)\n- [📜 License](#license)\n\n---\n\n\u003ca id=\"awesome-data-science-repositories\"\u003e\u003c/a\u003e\n\n## 🏆 Awesome Data Science Repositories\n\nCurated collections of high-quality GitHub repos for inspiration and learning.\n\n- [Awesome Data Science](https://github.com/academic/awesome-datascience) - A curated list of courses, books, tools, and resources for data science.\n- [Data Science for Beginners](https://github.com/microsoft/Data-Science-For-Beginners) - Microsoft's data science curriculum.  \n- [OSSU Data Science](https://github.com/ossu/data-science) - Open Source Society University's self-study path.  \n- [Data Science Best Resources](https://github.com/tirthajyoti/Data-science-best-resources) - Carefully curated links for data science resources in one place.\n- [Data Science Articles from CodeCut](https://github.com/CodeCutTech/Data-science) - A collection of articles, videos, and code related to data science.\n- [Data Science Using Python](https://github.com/WillKoehrsen/Data-Analysis) - Resources for data analysis using Python.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"roadmaps\"\u003e\u003c/a\u003e\n\n## 🗺️ Roadmaps\n\nStep-by-step guides and skill trees to master data science and analytics.\n\n- [Data Analyst Roadmap](https://roadmap.sh/data-analyst) - Structured learning path for analysts.\n- [Data Science Roadmap from A to Z](https://github.com/Moataz-Elmesmary/Data-Science-Roadmap) - Comprehensive roadmap for data science.\n- [Roadmap To Learn Data Science](https://github.com/krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025) - A comprehensive and updated roadmap for learning data science with modern tools and technologies.\n- [66DaysOfData](https://github.com/mrankitgupta/Data-Analyst-Roadmap) - 66-day data analytics learning challenge.\n- [Data Analyst Roadmap for Professionals](https://github.com/hemansnation/Data-Analyst-Roadmap) - 8-week program for analysts at all levels.\n- [Data Science Roadmap Tutorials](https://github.com/MrMimic/data-scientist-roadmap) - Tutorials for the data science roadmap.\n- [Data Analyst Roadmap from Zero](https://github.com/mtahiraslan/data-analyst-roadmap) - Guide to becoming a data analyst from scratch.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"python\"\u003e\u003c/a\u003e\n\n## 🐍 Python\n\n\u003ca id=\"python-resources\"\u003e\u003c/a\u003e\n\n### Resources\n\nA collection of resources for learning and mastering Python programming.\n\n- [Awesome Python](https://github.com/vinta/awesome-python) - An opinionated list of awesome Python frameworks, libraries, software, and resources.\n- [30 Days Of Python](https://github.com/Asabeneh/30-Days-Of-Python) - A 30-day programming challenge to learn the Python programming language.\n- [Real Python Tutorials](https://realpython.com/) - Tutorials on Python from Real Python.\n- [Awesome Python Data Science](https://github.com/krzjoa/awesome-python-data-science) - A curated list of Python resources for data science.\n- [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook) - Full text of the \"Python Data Science Handbook\" in Jupyter Notebooks.\n- [Interactive Coding Challenges](https://github.com/donnemartin/interactive-coding-challenges) - 120+ interactive Python coding interview challenges.\n- [Clean Code Python](https://github.com/zedr/clean-code-python) - Clean Code concepts adapted for Python.\n- [Best of Python](https://github.com/ml-tooling/best-of-python) - A ranked list of awesome Python open-source libraries and tools.\n- [GeeksforGeeks Python](https://www.geeksforgeeks.org/python-programming-language-tutorial/) - Python tutorial from GeeksforGeeks.\n- [W3Schools Python](https://www.w3schools.com/python/) - A beginner-friendly tutorial and reference for the Python programming language.\n- [Tanu N Prabhu Python](https://github.com/Tanu-N-Prabhu/Python/tree/master) - This repository helps you understand Python from scratch.\n- [Think Python](https://github.com/AllenDowney/ThinkPython) - Jupyter notebooks and other resources for Think Python by Allen Downey.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"python-data-manipulation-with-pandas-and-numpy\"\u003e\u003c/a\u003e\n\n### Data Manipulation with Pandas and Numpy\n\nTutorials and best practices for working with Pandas and Numpy.\n\n- [Awesome Pandas](https://github.com/tommyod/awesome-pandas) - A curated list of resources for using the Pandas library.\n- [100 data puzzles for pandas](https://github.com/ajcr/100-pandas-puzzles) - A collection of data puzzles to practice your Pandas skills.\n- [Pandas Tutor](https://pandastutor.com/) - Visualize Pandas operations step-by-step (perfect for beginners).\n- [Pandas Exercises](https://github.com/guipsamora/pandas_exercises) - Exercises designed to help you improve your Pandas skills.\n- [Pandas Cookbook](https://github.com/jvns/pandas-cookbook) - A cookbook with various recipes for using Pandas effectively.\n- [Hands-On Data Analysis with Pandas](https://github.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition) - Materials for following along with Hands-On Data Analysis with Pandas.\n- [Effective Pandas](https://github.com/TomAugspurger/effective-pandas) - A series focused on writing effective and idiomatic Pandas code.\n- [From Python to Numpy](https://github.com/rougier/from-python-to-numpy) - An open-access book on vectorization and efficient numerical computing with NumPy.\n- [NumPy 100 Exercises](https://github.com/rougier/numpy-100) - A collection of 100 exercises to master the NumPy library for scientific computing.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"python-useful-python-tools-for-data-analysis\"\u003e\u003c/a\u003e\n\n### Useful Python Tools for Data Analysis\n\nA collection of Python libraries for efficient data manipulation, cleaning, visualization, validation, and analysis.\n\n\u003ca id=\"python-data-processing-transformation\"\u003e\u003c/a\u003e\n\n#### Data Processing \u0026 Transformation\n\n- [Pandas](https://github.com/pandas-dev/pandas) - Powerful Python library for data analysis and manipulation with flexible data structures.\n- [NumPy](https://github.com/numpy/numpy) - Fundamental package for scientific computing in Python with multidimensional array support.\n- [Pandas DQ](https://github.com/AutoViML/pandas_dq) - Data type correction and automatic DataFrame cleaning.\n- [Vaex](https://github.com/vaexio/vaex) - High-performance Python library for lazy Out-of-Core DataFrames.\n- [Polars](https://github.com/pola-rs/polars) - Multithreaded, vectorized query engine for DataFrames.\n- [Fugue](https://github.com/fugue-project/fugue) - Unified interface for Pandas, Spark, and Dask.\n- [TheFuzz](https://github.com/seatgeek/thefuzz) - Fuzzy string matching (Levenshtein distance).\n- [DateUtil](https://github.com/dateutil/dateutil) - Extensions for standard Python datetime features.\n- [Arrow](https://github.com/arrow-py/arrow) - Enhanced work with dates and times.\n- [Pendulum](https://github.com/sdispater/pendulum) - Alternative to datetime with timezone support.\n- [Dask](https://github.com/dask/dask) - Parallel computing for arrays and DataFrames.\n- [Modin](https://github.com/modin-project/modin) - Speeds up Pandas by distributing computations.\n- [Pandarallel](https://github.com/nalepae/pandarallel) - Parallel operations for pandas DataFrames.\n- [DataCleaner](https://github.com/rhiever/datacleaner) - Python tool for automatically cleaning and preparing datasets.\n- [Pandas Flavor](https://github.com/Zsailer/pandas_flavor) - Add custom methods to Pandas.\n- [Pandas DataReader](https://github.com/pydata/pandas-datareader) - Reads data from various online sources into pandas DataFrames.\n- [Sklearn Pandas](https://github.com/scikit-learn-contrib/sklearn-pandas) - Bridge between Pandas and Scikit-learn.\n- [CuPy](https://github.com/cupy/cupy) - A NumPy-compatible array library accelerated by NVIDIA CUDA for high-performance computing.\n- [Numba](https://github.com/numba/numba) - A JIT compiler that translates a subset of Python and NumPy code into fast machine code.\n- [Pandas Stubs](https://github.com/pandas-dev/pandas-stubs) - Type stubs for pandas, improves IDE autocompletion.\n- [Petl](https://github.com/petl-developers/petl) - ETL tool for data cleaning and transformation.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"python-automated-data-visualization-tools\"\u003e\u003c/a\u003e\n\n#### Automated EDA and Visualization Tools\n\n- [AutoViz](https://github.com/AutoViML/AutoViz) - Automatic data visualization in 1 line of code.\n- [Sweetviz](https://github.com/fbdesignpro/sweetviz) - Automatic EDA with dataset comparison.\n- [Lux](https://github.com/lux-org/lux) - Automatic DataFrame visualization in Jupyter.\n- [YData Profiling](https://github.com/ydataai/ydata-profiling) - Data quality profiling \u0026 exploratory data analysis.\n- [Missingno](https://github.com/ResidentMario/missingno) - Visualize missing data patterns.\n- [Vizro](https://github.com/mckinsey/vizro) - Low-code toolkit for building data visualization apps.\n- [Yellowbrick](https://github.com/DistrictDataLabs/yellowbrick) - Visual diagnostic tools for machine learning.\n- [Great Tables](https://github.com/posit-dev/great-tables) - Create awesome display tables using Python.\n- [DataMapPlot](https://github.com/TutteInstitute/datamapplot) - Create beautiful plots of data maps.\n- [Datashader](https://github.com/holoviz/datashader) - Quickly and accurately render even the largest data.\n- [PandasAI](https://github.com/sinaptik-ai/pandas-ai) - Conversational data analysis using LLMs and RAG.\n- [Mito](https://github.com/mito-ds/mito) - Jupyter extensions for faster code writing.\n- [D-Tale](https://github.com/man-group/dtale) - Interactive GUI for data analysis in a browser.\n- [Pandasgui](https://github.com/adamerose/pandasgui) - GUI for viewing and filtering DataFrames.\n- [PyGWalker](https://github.com/Kanaries/pygwalker) - Interactive UIs for visual analysis of DataFrames.\n- [QGrid](https://github.com/quantopian/qgrid) - Interactive grid for DataFrames in Jupyter.\n- [Pivottablejs](https://github.com/nicolaskruchten/jupyter_pivottablejs) - Interactive PivotTable.js tables in Jupyter.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"python-data-quality-validation\"\u003e\u003c/a\u003e\n\n#### Data Quality \u0026 Validation\n\n- [PyOD](https://github.com/yzhao062/pyod) - Outlier and anomaly detection.\n- [Alibi Detect](https://github.com/SeldonIO/alibi-detect) - Outlier, adversarial and drift detection.\n- [Pandera](https://github.com/unionai-oss/pandera) - Data validation through declarative schemas.\n- [Cerberus](https://github.com/pyeve/cerberus) - Data validation through schemas.\n- [Pydantic](https://github.com/pydantic/pydantic) - Data validation using Python type annotations.\n- [Dora](https://github.com/NathanEpstein/Dora) - Automate EDA: preprocessing, feature engineering, visualization.\n- [Great Expectations](https://github.com/great-expectations/great_expectations) - Data validation and testing.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"python-feature-engineering-selection\"\u003e\u003c/a\u003e\n\n#### Feature Engineering \u0026 Selection\n\n- [FeatureTools](https://github.com/alteryx/featuretools) - Automated feature engineering.\n- [Feature Engine](https://github.com/feature-engine/feature_engine) - Feature engineering with Scikit-Learn compatibility.\n- [Prince](https://github.com/MaxHalford/prince) - Multivariate exploratory data analysis (PCA, CA, MCA).\n- [Fitter](https://github.com/cokelaer/fitter) - Figures out the distribution your data comes from.\n- [Feature Selector](https://github.com/WillKoehrsen/feature-selector) - Tool for dimensionality reduction of machine learning datasets.\n- [Category Encoders](https://github.com/scikit-learn-contrib/category_encoders) - Extensive collection of categorical variable encoders.\n- [Imbalanced Learn](https://github.com/scikit-learn-contrib/imbalanced-learn) - Handling imbalanced datasets.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"python-specialized-data-tools\"\u003e\u003c/a\u003e\n\n#### Specialized Data Tools\n\n- [cuDF](https://github.com/rapidsai/cudf) - A GPU DataFrame library for loading, joining, and aggregating data.\n- [Faker](https://github.com/joke2k/faker) - Generates fake data for testing.\n- [Mimesis](https://github.com/lk-geimfari/mimesis) - Generates realistic test data.\n- [Geopy](https://github.com/geopy/geopy) - Geocoding addresses and calculating distances.\n- [PySAL](https://github.com/pysal/pysal) - Spatial analysis functions.\n- [Scattertext](https://github.com/JasonKessler/scattertext) - Beautiful visualizations of language differences among document types.\n- [IGraph](https://github.com/igraph/igraph) - A library for creating and manipulating graphs and networks, with bindings for multiple languages.\n- [Joblib](https://github.com/joblib/joblib) - A lightweight pipelining library for Python, particularly useful for saving and loading large NumPy arrays.\n- [ImageIO](https://github.com/imageio/imageio) - A library that provides an easy interface to read and write a wide range of image data.\n- [Texthero](https://github.com/jbesomi/texthero) - Text preprocessing, representation and visualization.\n- [Geopandas](https://github.com/geopandas/geopandas) - Geographic data operations with pandas.\n- [NetworkX](https://github.com/networkx/networkx) - Network analysis and graph theory.\n- [Chardet](https://github.com/chardet/chardet) - Python library to detect the character encoding of text and files.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"sql-databases\"\u003e\u003c/a\u003e\n\n## 🗃️ SQL \u0026 Databases\n\n\u003ca id=\"sql-databases-resources\"\u003e\u003c/a\u003e\n\n### Resources\n\nSQL tutorials and database design principles.\n\n- [SQLZoo - SQL Tutorial](https://sqlzoo.net/wiki/SQL_Tutorial) - Interactive SQL tutorial.\n- [SQL Bolt - Learn SQL](https://sqlbolt.com/) - Learn SQL through interactive lessons.\n- [SQL Tutorial](https://www.sqltutorial.org/) - Comprehensive SQL tutorial resource.\n- [SQL Tutorial by W3Schools.](https://www.w3schools.com/sql/default.asp) - Comprehensive SQL tutorial.\n- [PostgreSQL Tutorial by W3Resource](https://w3resource.com/PostgreSQL/tutorial.php) - Tutorial for PostgreSQL.\n- [MySQL Tutorial by W3Resource](https://www.w3resource.com/mysql/mysql-tutorials.php) - Tutorial for MySQL.\n- [MongoDB Tutorial by W3Resource](https://www.w3resource.com/mongodb/nosql.php) - Tutorial for MongoDB.\n- [EverSQL](https://www.eversql.com/) - AI-powered SQL query optimization and database observability tool.\n- [Awesome Database Learning](https://github.com/pingcap/awesome-database-learning) - Educational resources on database internals, distributed systems, and storage.\n- [Awesome Postgres](https://github.com/dhamaniasad/awesome-postgres) - A curated list of awesome PostgreSQL software, libraries, tools and resources.\n- [Awesome MySql](https://github.com/shlomi-noach/awesome-mysql) - A curated list of awesome MySQL software, libraries, tools and resources.\n- [Awesome Clickhouse](https://github.com/korchasa/awesome-clickhouse) - A curated list of awesome ClickHouse software.\n- [Awesome MongoDB](https://github.com/ramnes/awesome-mongodb) - A curated list of awesome MongoDB resources, libraries, tools, and applications.\n- [Awesome Duckdb](https://github.com/davidgasquez/awesome-duckdb) - Curated tools, resources, and extensions for DuckDB analytical database.\n- [Awesome SQLAlchemy](https://github.com/dahlia/awesome-sqlalchemy) - A curated list of awesome tools for SQLAlchemy.\n- [Awesome Sql](https://github.com/danhuss/awesome-sql) - List of tools and techniques for working with relational databases.\n- [AnimateSQL](https://animatesql.com/) - Interactive tool that visualizes the step-by-step execution of SQL queries.\n- [SQL Tips and Tricks](https://github.com/ben-nour/SQL-tips-and-tricks) - Useful SQL techniques and optimizations for data analysis.\n- [Practice Window Functions](https://www.practicewindowfunctions.com) - Free interactive SQL tutorial site focused on mastering window functions through 80+ hands-on problems with hints and solutions.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"sql-databases-engines\"\u003e\u003c/a\u003e\n\n### Databases\n\nPopular open-source database systems for a variety of use cases and data models.\n\n- [SQLite](https://github.com/sqlite/sqlite) – Embedded, file‑based SQL engine, the most used database in the world.\n- [PostgreSQL](https://github.com/postgres/postgres) – Advanced open‑source relational database with extensibility and SQL compliance.\n- [MySQL](https://github.com/mysql/mysql-server) – Widely used relational database management system (Oracle’s community version).\n- [MariaDB](https://github.com/MariaDB/server) – Community developed fork of MySQL with enhanced features.\n- [DuckDB](https://github.com/duckdb/duckdb) – In‑process analytical database designed for fast OLAP queries.\n- [ClickHouse](https://github.com/ClickHouse/ClickHouse) – Columnar database for real‑time analytics on large datasets.\n- [Apache Cloudberry](https://github.com/apache/cloudberry) - A mature open-source MPP database evolved from Greenplum with a newer PostgreSQL kernel.\n- [TimescaleDB](https://github.com/timescale/timescaledb) – Time‑series SQL database built on PostgreSQL.\n- [TDengine](https://github.com/taosdata/TDengine) – Time‑series database for IoT, vehicles, and industrial monitoring.\n- [MongoDB](https://github.com/mongodb/mongo) – Document database with a flexible, JSON‑like data model.\n- [Cassandra](https://github.com/apache/cassandra) – Highly scalable, distributed NoSQL database for large‑scale deployments.\n- [Redis](https://github.com/redis/redis) – In‑memory data structure store used as database, cache, and message broker.\n- [CockroachDB](https://github.com/cockroachdb/cockroach) – Distributed SQL database with strong consistency and horizontal scaling.\n- [Neo4j](https://github.com/neo4j/neo4j) – Native graph database for connected data and relationships.\n- [InfluxDB](https://github.com/influxdata/influxdb) – Purpose‑built time‑series database for metrics and events.\n- [ScyllaDB](https://github.com/scylladb/scylla) – Drop‑in Cassandra alternative written in C++ for higher performance.\n- [Apache HBase](https://github.com/apache/hbase) – Distributed, scalable, big data store modeled after Google's Bigtable, running on top of HDFS.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"sql-databases-tools\"\u003e\u003c/a\u003e\n\n### Tools\n\nA collection of libraries and drivers for seamless database access and interaction.\n\n- [PyODBC](https://github.com/mkleehammer/pyodbc) - Python library for ODBC database access.\n- [SQLAlchemy](https://github.com/sqlalchemy/sqlalchemy) - SQL toolkit and ORM for Python.\n- [Psycopg2](https://github.com/psycopg/psycopg2) - PostgreSQL database adapter.\n- [MySQL Connector/Python](https://github.com/mysql/mysql-connector-python) - MySQL driver for Python.\n- [PonyORM](https://github.com/ponyorm/pony) - ORM for Python with dynamic query generation.\n- [PyMongo](https://github.com/mongodb/mongo-python-driver) - Official MongoDB driver for Python.\n- [SQLiteviz](https://github.com/lana-k/sqliteviz) - A tool for exploring SQLite databases and visualizing the results of your queries.\n- [DB Browser for SQLite](https://github.com/sqlitebrowser/sqlitebrowser) - A high quality, visual, open source tool to create, design, and edit database files compatible with SQLite.\n- [DBeaver](https://github.com/dbeaver/dbeaver) - A free universal database tool and SQL client for developers, SQL programmers, and administrators.\n- [Beekeeper Studio](https://github.com/beekeeper-studio/beekeeper-studio) - A modern, easy-to-use SQL client and database manager with a clean, cross-platform interface.\n- [DBConvert Streams](https://streams.dbconvert.com/) - Database IDE for exploring, migrating, and syncing PostgreSQL, MySQL, files, and S3.\n- [SQLFluff](https://github.com/sqlfluff/sqlfluff) - A modular SQL linter and auto-formatter designed to enforce consistent style and catch errors in SQL code.\n- [PyMySQL](https://github.com/PyMySQL/PyMySQL) - A pure-Python MySQL client library for interacting with MySQL databases from Python applications.\n- [Vanna.AI](https://github.com/vanna-ai/vanna) - An AI-powered tool for generating SQL queries from natural language questions.\n- [SQLChat](https://github.com/sqlchat/sqlchat) - A chat-based SQL client that allows you to query databases using natural language conversations.\n- [Records](https://github.com/kennethreitz-archive/records) - SQL queries to databases via Python syntax.\n- [Dataset](https://github.com/pudo/dataset) - JSON-like interface for working with SQL databases.\n- [SQLGlot](https://github.com/tobymao/sqlglot) - A no-dependency SQL parser, transpiler, and optimizer for Python.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"data-visualization\"\u003e\u003c/a\u003e\n\n## 📊 Data Visualization\n\n\u003ca id=\"data-visualization-resources\"\u003e\u003c/a\u003e\n\n### Resources\n\nColor theory, chart selection guides, and storytelling tips.\n\n- [From Data to Viz](https://github.com/holtzy/data_to_viz) - A guide to choosing the right visualization based on your data.\n- [Awesome DataViz](https://github.com/hal9ai/awesome-dataviz) - A curated list of awesome data visualization libraries, tools, and resources.\n- [Visualization Curriculum](https://github.com/uwdata/visualization-curriculum) - Interactive notebooks designed to teach data visualization concepts.\n- [Scientific Visualization Book](https://github.com/rougier/scientific-visualization-book) - Guide to creating effective scientific visualizations and plots.\n- [The Python Graph Gallery](https://python-graph-gallery.com/) - A collection of Python graph examples for data visualization.\n- [FlowingData](https://flowingdata.com/) - Insights on data analysis and visualization.\n- [Data Visualization Catalogue](https://datavizcatalogue.com/index.html) - A comprehensive catalog of data visualization types.\n- [Data Viz Project](https://datavizproject.com/) - A resource for selecting suitable visualizations.\n- [Chartopedia](https://www.anychart.com/chartopedia/usage-type/) - A guide to help you select the appropriate chart types.\n- [DataForVisualization](https://dataforvisualization.com/) - Tutorials and insights on data visualization techniques.\n- [Truth \u0026 Beauty](https://truth-and-beauty.net/) - Exploration of the aesthetics of data visualization.\n- [Cedric Scherer's DataViz Resources](https://www.cedricscherer.com/top/dataviz/) - A collection of top data visualization resources and inspiration.\n- [Information is Beautiful](https://informationisbeautiful.net/) - A site dedicated to visualizations that make complex ideas clear and engaging.\n- [Plottie](https://plottie.art/) - A vast library of scientific plots for visualization inspiration and ideas.\n- [Friends Don't Let Friends](https://github.com/cxli233/FriendsDontLetFriends) - A collection of bad data visualization practices and better alternatives.\n- [Natural Colours](https://www.c82.net/natural-colors/) - A digital archive of historical color systems and pigments.\n- [Colorgorical](http://vrl.cs.brown.edu/color) - Resource for generating categorical color palettes using perceptual principles.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"data-visualization-tools\"\u003e\u003c/a\u003e\n\n### Tools\n\nLibraries for static, interactive, and 3D visualizations.\n\n- [Matplotlib](https://matplotlib.org/stable/contents.html) - A comprehensive library for creating static, animated, and interactive visualizations in Python.\n- [Seaborn](https://seaborn.pydata.org/) - A statistical data visualization library based on Matplotlib.\n- [Plotly](https://plotly.com/python/) - A library for creating interactive plots and dashboards.\n- [Altair](https://github.com/vega/altair) - A declarative statistical visualization library for Python.\n- [Bokeh](https://docs.bokeh.org/en/latest/) - A library for creating interactive visualizations for modern web browsers.\n- [HoloViews](https://holoviews.org/) - A tool for building complex visualizations easily.\n- [Geopandas](https://geopandas.org/en/stable/) - An extension of Pandas for geospatial data.\n- [Folium](https://python-visualization.github.io/folium/) - A library for visualizing data on interactive maps.\n- [Pygal](https://pygal.org/en/stable/) - A Python SVG charting library.\n- [Plotnine](https://plotnine.readthedocs.io/en/stable/) - A grammar of graphics for Python.\n- [Bqplot](https://github.com/bqplot/bqplot) - A plotting library for IPython/Jupyter notebooks.\n- [PyPalettes](https://github.com/JosephBARBIERDARNAL/pypalettes) - A large (+2500) collection of color maps for Python.\n- [Deck.gl](https://github.com/visgl/deck.gl) - A WebGL-powered framework for visual exploratory data analysis of large datasets.\n- [Python for Geo](https://github.com/geopandas/contextily) - Contextily: add background basemaps to your plots in GeoPandas.\n- [OSMnx](https://github.com/gboeing/osmnx) - A package to easily download, model, analyze, and visualize street networks from OpenStreetMap.\n- [Apache ECharts](https://github.com/apache/echarts) - A powerful, interactive charting and visualization library for browser-based applications.\n- [VisPy](https://github.com/vispy/vispy) - A high-performance interactive 2D/3D data visualization library leveraging the power of OpenGL.\n- [Glumpy](https://github.com/glumpy/glumpy) - A Python library for scientific visualization that is fast, scalable and beautiful, based on OpenGL.\n- [Pandas-bokeh](https://github.com/PatrikHlobil/Pandas-Bokeh) - Bokeh plotting backend for Pandas.\n- [QGIS](https://github.com/qgis/QGIS) - Free, open source, cross-platform geographic information system (GIS).\n- [Flourish](https://flourish.studio/) - Platform for creating interactive data visualizations and stories without coding.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"dashboards\"\u003e\u003c/a\u003e\n\n## 📈 Dashboards \u0026 BI\n\n\u003ca id=\"dashboards-resources\"\u003e\u003c/a\u003e\n\n### Resources\n\nTtutorials for building and enhancing dashboards and visualizations using various tools and frameworks.\n\n- [Awesome Dashboards](https://github.com/obazoud/awesome-dashboard) - A collection of outstanding dashboard and visualization resources.\n- [Best of Streamlit](https://github.com/jrieke/best-of-streamlit) - Showcase of community-built Streamlit applications.\n- [Awesome Dash](https://github.com/ucg8j/awesome-dash) - Comprehensive resources for Dash users.\n- [Awesome Panel](https://github.com/awesome-panel/awesome-panel) - Resources and support for Panel users.\n- [Awesome Streamlit](https://github.com/MarcSkovMadsen/awesome-streamlit) - Curated list of Streamlit resources and components.\n- [Dash Enterprise Samples](https://github.com/plotly/dash-sample-apps) - Production-ready Dash apps.\n- [geeksforgeeks - Tableau Tutorial](https://www.geeksforgeeks.org/tableau-tutorial/) - Comprehensive tutorial on Tableau.\n- [geeksforgeeks - Power BI Tutorial](https://www.geeksforgeeks.org/power-bi-tutorial/) - Detailed tutorial on Power BI.\n- [Tableau Public Gallery](https://public.tableau.com/app/discover) - A curated collection of real-world interactive dashboards to inspire and learn from.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"dashboards-tools\"\u003e\u003c/a\u003e\n\n### Tools\n\nFrameworks for building custom dashboard solutions.\n\n- [Dash](https://github.com/plotly/dash) - Framework for creating interactive web applications.\n- [Streamlit](https://github.com/streamlit/streamlit) - Simplified framework for building data applications.\n- [Panel](https://github.com/holoviz/panel) - Python library for creating custom interactive web apps and dashboards.\n- [Gradio](https://github.com/gradio-app/gradio) - Tool for creating and sharing machine learning applications.\n- [OpenSearch Dashboards](https://github.com/opensearch-project/OpenSearch-Dashboards) - A powerful data visualization and dashboarding tool for OpenSearch data, forked from Kibana.\n- [GridStack.js](https://github.com/gridstack/gridstack.js) - A library for building draggable, resizable responsive dashboard layouts.\n- [Tremor](https://github.com/tremorlabs/tremor-npm) - A React library to build dashboards fast with pre-built components for charts, KPIs, and more.\n- [Appsmith](https://github.com/appsmithorg/appsmith) - An open-source platform to build and deploy internal tools, admin panels, and CRUD apps quickly.\n- [Grafanalib](https://github.com/weaveworks/grafanalib) - A Python library for generating Grafana dashboards configuration as code.\n- [H2O Wave](https://github.com/h2oai/wave) - A Python framework for rapidly building and deploying realtime web apps and dashboards for AI and analytics.\n- [Shiny for Python](https://github.com/posit-dev/py-shiny) - Python version of the popular R Shiny framework.\n- [Voilà](https://github.com/voila-dashboards/voila) - Turn Jupyter notebooks into standalone web applications.\n- [Reflex](https://github.com/reflex-dev/reflex) - Full-stack Python framework for building web apps.\n- [Taipy](https://github.com/Avaiga/taipy) - Python library for building web applications and interactive dashboards.\n- [Evidence](https://github.com/evidence-dev/evidence) - Business intelligence platform that uses SQL and Markdown for reports.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"dashboards-software\"\u003e\u003c/a\u003e\n\n### Software\n\nA list of leading tools and platforms for data visualization and dashboard creation.\n\n- [Tableau](https://www.tableau.com) - Leading data visualization software.\n- [Microsoft Power BI](https://powerbi.microsoft.com) - Business analytics tool for visualizing data.\n- [QlikView](https://www.qlik.com/us/products/qlikview) - Tool for data visualization and business intelligence.\n- [Metabase](https://www.metabase.com) - User-friendly open-source BI tool.\n- [Apache Superset](https://superset.apache.org) - Open-source data exploration and visualization platform.\n- [Preset](https://preset.io/) - A platform for modern business intelligence, providing a hosted version of Apache Superset.\n- [Metabase](https://github.com/metabase/metabase) - The simplest way to get analytics and business intelligence for everyone in your company.\n- [Redash](https://github.com/getredash/redash) - Tool for visualizing and sharing data insights.\n- [Grafana](https://grafana.com) - Dashboarding and monitoring tool.\n- [Datawrapper](https://github.com/datawrapper/datawrapper) - User-friendly chart and map creation tool.\n- [ChartBlocks](https://www.chartblocks.com) - Online chart creation platform.\n- [Infogram](https://infogram.com) - Tool for creating infographics and visual content.\n- [Google Data Studio](https://datastudio.google.com) - Free tool for creating interactive dashboards and reports.\n- [Rath](https://github.com/Kanaries/Rath) - Next-generation automated data exploratory analysis and visualization platform.\n- [Kibana](https://github.com/elastic/kibana) - The official visualization and dashboarding tool for the Elastic Stack (Elasticsearch, Logstash, Beats).\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"web-scraping-crawling\"\u003e\u003c/a\u003e\n\n## 🕸️ Web Scraping \u0026 Crawling\n\n\u003ca id=\"web-scraping-crawling-resources\"\u003e\u003c/a\u003e\n\n### Resources\n\nA collection of valuable resources, tutorials, and libraries for web scraping with Python.\n\n- [Awesome Web Scraping](https://github.com/lorien/awesome-web-scraping) - List of libraries, tools, and APIs for web scraping and data processing.\n- [Python Scraping](https://github.com/REMitchell/python-scraping) - Code samples from the book \"Web Scraping with Python\".\n- [Scraping Tutorial](https://github.com/Blatzar/scraping-tutorial) - Tutorial for scraping streaming sites.\n- [Webscraping from 0 to Hero](https://github.com/TheWebScrapingClub/webscraping-from-0-to-hero) - An open project repository sharing knowledge and experiences about web scraping with Python.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"web-scraping-crawling-tools\"\u003e\u003c/a\u003e\n\n### Tools\n\nA list of libraries and tools for web scraping.\n\n- [Requests](https://github.com/psf/requests) - A simple, yet elegant, HTTP library for Python.\n- [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/) - A library for parsing HTML and XML documents.\n- [Selenium](https://github.com/SeleniumHQ/selenium) - A tool for automating web applications for testing purposes.\n- [Scrapy](https://scrapy.org/) - An open-source and collaborative web crawling framework for Python.\n- [Browser Use](https://github.com/browser-use/browser-use) - A library for browser automation and web scraping.\n- [Gerapy](https://github.com/Gerapy/Gerapy) - Distributed Crawler Management Framework based on Scrapy, Scrapyd, Django, and Vue.js.\n- [AutoScraper](https://github.com/alirezamika/autoscraper) - A smart, automatic, fast, and lightweight web scraper for Python.\n- [Feedparser](https://github.com/kurtmckee/feedparser) - A library to parse feeds in Python.\n- [Trafilatura](https://github.com/adbar/trafilatura) - A Python \u0026 command-line tool to gather text and metadata on the web.\n- [You-Get](https://github.com/soimort/you-get) - A tiny command-line utility to download media contents (videos, audios, images) from the web.\n- [MechanicalSoup](https://github.com/MechanicalSoup/MechanicalSoup) - A Python library for automating interaction with websites.\n- [ScrapeGraph AI](https://github.com/ScrapeGraphAI/Scrapegraph-ai) - A Python scraper based on AI.\n- [Snscrape](https://github.com/JustAnotherArchivist/snscrape) - A social networking service scraper in Python.\n- [Ferret](https://github.com/MontFerret/ferret) - A web scraping system that lets you declaratively describe what data to extract using a simple query language.\n- [Grab](https://github.com/lorien/grab) - A Python framework for building web scraping apps, providing a high-level API for asynchronous requests.\n- [Playwright](https://github.com/microsoft/playwright-python) - Python version of the Playwright browser automation library.\n- [PyQuery](https://github.com/gawel/pyquery) - A jQuery-like library for parsing HTML documents in Python.\n- [Helium](https://github.com/mherrmann/helium) - High-level Selenium wrapper for easier web automation.\n- [Scrapling](https://github.com/D4Vinci/Scrapling) - A framework for building web scrapers and crawlers.\n- [Crawl4AI](https://github.com/unclecode/crawl4ai) - Advanced web crawling framework designed for AI and data extraction tasks.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"mathematics\"\u003e\u003c/a\u003e\n\n## 🔢 Mathematics\n\nA collection of resources for learning mathematics, particularly in the context of data science and machine learning.\n\n- [Awesome Math](https://github.com/rossant/awesome-math) - A curated list of mathematics resources, books, and online courses.\n- [MML Bool](https://github.com/mml-book/mml-book.github.io) - Comprehensive resource for mathematics in machine learning.\n- [3Blue1Brown](https://www.3blue1brown.com/) - Visual explanations of mathematical concepts through animated videos.\n- [Immersive Linear Algebra](http://immersivemath.com/ila/) - Interactive resource for understanding linear algebra.\n- [Hackermath](https://github.com/amitkaps/hackermath) - Resource for learning statistics and mathematics for data science.\n- [Stats Maths with Python](https://github.com/tirthajyoti/Stats-Maths-with-Python) - Collection of Python scripts and notebooks for statistics and mathematics.\n- [Fast.ai - Computational Linear Algebra](https://github.com/fastai/numerical-linear-algebra) - Resource for learning linear algebra computationally.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"statistics-probability\"\u003e\u003c/a\u003e\n\n## 🎲 Statistics \u0026 Probability\n\n\u003ca id=\"statistics-probability-resources\"\u003e\u003c/a\u003e\n\n### Resources\n\nA selection of resources focused on statistics and probability, including tutorials and comprehensive guides.\n\n- [Awesome Statistics](https://github.com/erikgahner/awesome-statistics) - A curated list of statistics resources, software, and learning materials.\n- [The Elements of Statistical Learning](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks) - Notebooks for understanding statistical learning concepts.\n- [Seeing Theory](https://github.com/seeingtheory/Seeing-Theory) - Interactive visual resource for learning probability and statistics.\n- [Code repository for O'Reilly book](https://github.com/gedeck/practical-statistics-for-data-scientists) - Companion code for a practical statistics book.\n- [Statistical Learning Theory - Stanford University](https://web.stanford.edu/class/cs229t/notes.pdf) - Lecture notes on statistical learning theory.\n- [StatLect](https://www.statlect.com/) - Comprehensive online textbook covering probability and statistics concepts.\n- [stanford.edu - Probabilities and Statistics](https://stanford.edu/~shervine/teaching/cs-229/refresher-probabilities-statistics) - Refresher course on probabilities and statistics from Stanford University.\n- [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - Resource for learning Bayesian methods in Python.\n- [Bayesian Modeling and Computation in Python](https://github.com/BayesianModelingandComputationInPython/BookCode_Edition1) - Code for the book \"Bayesian Modeling and Computation in Python\".\n- [Stat Trek](https://stattrek.com/) - A resource for learning statistics and probability, with tutorials and tools.\n- [Online Statistics Book](https://onlinestatbook.com/2/index.html) - An interactive online statistics book with simulations and demonstrations.\n- [All of Statistics](https://github.com/telmo-correa/all-of-statistics) - Resource for studying statistics based on Wasserman's book.\n- [Think Stats](https://github.com/AllenDowney/ThinkStats/tree/v3) - Book and code for an introduction to Probability and Statistics.\n- [Think Bayes 2](https://github.com/AllenDowney/ThinkBayes2) - Book and code for Bayesian statistical methods.\n- [Causal Inference: The Mixtape](https://mixtape.scunning.com/) - Practical guide to causal inference methods.\n- [The Effect](https://theeffectbook.net/) - Modern introduction to causality and research design.\n- [The Statistics Handbook](https://github.com/carloocchiena/the_statistics_handbook) - Open-source statistics hands-on handbook.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"statistics-probability-tools\"\u003e\u003c/a\u003e\n\n### Tools\n\nA collection of tools focused on statistics and probability.\n\n- [SciPy](https://github.com/scipy/scipy) - Fundamental library for scientific computing and statistics.\n- [Statsmodels](https://github.com/statsmodels/statsmodels) - Statistical modeling, testing, and data exploration.\n- [PyMC](https://github.com/pymc-devs/pymc) - A probabilistic programming library for Python that allows for flexible Bayesian modeling.\n- [Pingouin](https://github.com/raphaelvallat/pingouin) - Statistical package with improved usability over SciPy.\n- [scikit-posthocs](https://github.com/maximtrp/scikit-posthocs) - Post-hoc tests for statistical analysis of data.\n- [Lifelines](https://github.com/CamDavidsonPilon/lifelines) - Survival analysis and event history analysis in Python.\n- [scikit-survival](https://github.com/sebp/scikit-survival) - Survival analysis built on scikit-learn for time-to-event prediction.\n- [Bootstrap](https://github.com/cgevans/scikits-bootstrap) - Bootstrap confidence interval estimation methods.\n- [PyStan](https://github.com/stan-dev/pystan) - Python interface to Stan for Bayesian statistical modeling.\n- [ArviZ](https://github.com/arviz-devs/arviz) - Exploratory analysis of Bayesian models with visual diagnostics.\n- [PyGAM](https://github.com/dswah/pyGAM) - A Python library for generalized additive models with built-in smoothing and regularization.\n- [NumPyro](https://github.com/pyro-ppl/numpyro) - A probabilistic programming library built on JAX for high-performance Bayesian modeling.\n- [Causal Impact](https://github.com/WillianFuks/tfcausalimpact) - A Python implementation of the R package for causal inference using Bayesian structural time-series models.\n- [DoWhy](https://github.com/py-why/dowhy) - A Python library for causal inference that supports explicit modeling and testing of causal assumptions.\n- [Patsy](https://github.com/pydata/patsy) - A Python library for describing statistical models and building design matrices.\n- [Pomegranate](https://github.com/jmschrei/pomegranate) - Fast and flexible probabilistic modeling library for Python with GPU support.\n- [Pgmpy](https://github.com/pgmpy/pgmpy) - Python library for probabilistic and causal inference using graphical models.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"ab-testing\"\u003e\u003c/a\u003e\n\n## 🧪 A/B Testing\n\nA collection of resources focused on A/B testing.\n\n- [DynamicYield A/B Testing](https://www.dynamicyield.com/course/testing-and-optimization/) - An online course covering advanced testing and optimization techniques.\n- [Evan's Awesome A/B Tools](https://www.evanmiller.org/ab-testing/) - A/B test calculators.\n- [Experimentguide](https://experimentguide.com/) - A practical guide to A/B testing and experimentation from industry leaders.\n- [Google's A/B Testing Course](https://www.udacity.com/course/ab-testing--ud257) - A free Udacity course covering the fundamentals of A/B testing.\n- [So You Think You Can Test?](https://www.lukasvermeer.nl/confidence/) - Experience the challenges of A/B testing through this educational simulation.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"time-series-analysis\"\u003e\u003c/a\u003e\n\n## ⏳ Time Series Analysis\n\n\u003ca id=\"time-series-analysis-resources\"\u003e\u003c/a\u003e\n\n### Resources\n\nA collection of resources for understanding time series fundamentals and analytical techniques.\n\n- [Awesome Time Series](https://github.com/lmmentel/awesome-time-series) - A curated list of resources dedicated to time series analysis and forecasting.\n- [Forecasting: Principles and Practice](https://otexts.com/fpp3/) - Comprehensive textbook on forecasting methods with practical examples.\n- [NIST/SEMATECH e-Handbook](https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4.htm) - Official time series analysis guide from NIST.\n- [Awesome Time Series Anomaly Detection](https://github.com/rob-med/awesome-TS-anomaly-detection) - A curated list of tools, datasets, and papers dedicated to time series anomaly detection.\n- [Awesome Time Series in Python](https://github.com/MaxBenChrist/awesome_time_series_in_python) - A comprehensive list of Python tools and libraries for time series analysis.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"time-series-analysis-tools\"\u003e\u003c/a\u003e\n\n### Tools\n\nA collection of tools for working with temporal data.\n\n- [Facebook Prophet](https://github.com/facebook/prophet) - A procedure for forecasting time series data based on an additive model.\n- [Uber Orbit](https://github.com/uber/orbit) - A Python package for Bayesian time series forecasting and inference.\n- [sktime](https://github.com/sktime/sktime) - A unified Python framework for machine learning with time series, compatible with scikit-learn.\n- [GluonTS](https://github.com/awslabs/gluonts) - A Python toolkit for probabilistic time series modeling, built on MXNet.\n- [Time-Series-Library](https://github.com/thuml/Time-Series-Library) - A library for deep learning-based time series analysis and forecasting.\n- [TimesFM](https://github.com/google-research/timesfm) - A pretrained time series foundation model from Google Research for zero-shot forecasting.\n- [PyTorch Forecasting](https://github.com/sktime/pytorch-forecasting) - A PyTorch-based library for time series forecasting with neural networks.\n- [Time-series-prediction](https://github.com/LongxingTan/Time-series-prediction) - A collection of time series prediction methods and implementations.\n- [PlotJuggler](https://github.com/facontidavide/PlotJuggler) - A tool to visualize and analyze time series data logs in real-time.\n- [TSFresh](https://github.com/blue-yonder/tsfresh) - Automatically extracting features from time series data.\n- [pmdarima](https://github.com/alkaline-ml/pmdarima) - Python library for ARIMA modeling and time series analysis.\n- [Kats](https://github.com/facebookresearch/Kats) - Toolkit for analyzing time series data from Facebook Research.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"data-engineering\"\u003e\u003c/a\u003e\n\n## ⚙️ Data Engineering\n\n\u003ca id=\"data-engineering-resources\"\u003e\u003c/a\u003e\n\n### Resources\n\nA collection of resources to help you build and manage robust data pipelines and infrastructure.\n\n- [Data Engineer Handbook](https://github.com/DataExpert-io/data-engineer-handbook) - A comprehensive guide covering fundamental and advanced data engineering concepts.\n- [Data Engineering Zoomcamp](https://github.com/DataTalksClub/data-engineering-zoomcamp) - Free course on data engineering fundamentals.\n- [Awesome Data Engineering](https://github.com/igorbarinov/awesome-data-engineering) - A curated list of data engineering tools, software, and resources.\n- [Data Engineering Cookbook](https://github.com/andkret/Cookbook) - Techniques and strategies for building reliable data platforms.\n- [Awesome Pipeline](https://github.com/pditommaso/awesome-pipeline) - A curated list of pipeline toolkits for data processing and workflow management.\n- [Awesome DB Tools](https://github.com/mgramin/awesome-db-tools) - A curated list of awesome database tools.\n- [Awesome Kafka](https://github.com/infoslack/awesome-kafka) - Curated resources for learning and working with Apache Kafka: books, trainings, tools.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"data-engineering-tools\"\u003e\u003c/a\u003e\n\n### Tools\n\nA collection of tools for building, deploying, and managing data pipelines and infrastructure.\n\n- [dbt-core](https://github.com/dbt-labs/dbt-core) - A framework for transforming data in your warehouse using SQL and Jinja.\n- [Apache Spark](https://github.com/apache/spark) - A unified engine for large-scale data processing and analytics.\n- [Apache Kafka](https://github.com/apache/kafka) - A distributed event streaming platform for building real-time data pipelines.\n- [Dagster](https://github.com/dagster-io/dagster) - A data orchestrator for machine learning, analytics, and ETL.\n- [Apache Airflow](https://github.com/apache/airflow) - A platform to programmatically author, schedule, and monitor workflows.\n- [Apache Hive](https://github.com/apache/hive) - A data warehouse software for reading, writing, and managing large datasets in distributed storage using SQL.\n- [Apache Hadoop](https://github.com/apache/hadoop) - A framework that allows for the distributed processing of large data sets across clusters of computers.\n- [Luigi](https://github.com/spotify/luigi) - A Python module for building complex and batch-oriented data pipelines.\n- [Apache Iceberg](https://github.com/apache/iceberg) - A high-performance table format for huge analytic datasets.\n- [Apache Cassandra](https://github.com/apache/cassandra) - A highly scalable distributed NoSQL database designed for handling large amounts of data across many commodity servers.\n- [Apache Flink](https://github.com/apache/flink) - A framework for stateful computations over unbounded and bounded data streams (real-time stream processing).\n- [Apache Beam](https://github.com/apache/beam) - A unified model for defining both batch and streaming data-parallel processing pipelines.\n- [Apache Pulsar](https://github.com/apache/pulsar) - A cloud-native, distributed messaging and streaming platform.\n- [Delta Lake](https://github.com/delta-io/delta) - A storage layer that brings ACID transactions to Apache Spark and big data workloads.\n- [Apache Hudi](https://github.com/apache/hudi) - An open data lakehouse platform, built on a high-performance open table format.\n- [Trino](https://github.com/trinodb/trino) - A distributed SQL query engine designed for fast analytic queries against large datasets.\n- [DataHub](https://github.com/datahub-project/datahub) - A metadata platform for the modern data stack.\n- [OpenLineage](https://github.com/OpenLineage/OpenLineage) - An open framework for collection and analysis of data lineage.\n- [Kedro](https://github.com/kedro-org/kedro) - A framework for creating reproducible, maintainable and modular data science code.\n- [Apache Calcite](https://github.com/apache/calcite) - A dynamic data management framework that allows for SQL parsing, optimization, and federation.\n- [Prefect](https://github.com/PrefectHQ/prefect) - Workflow orchestration for building resilient data pipelines.\n- [Apache Arrow](https://github.com/apache/arrow) - Universal columnar format and multi-language toolbox for fast data interchange.\n- [Kestra](https://github.com/kestra-io/kestra) - An open-source, event-driven orchestrator that simplifies data workflow management.\n- [Conductor](https://github.com/conductor-oss/conductor) - Orchestration engine for running complex, multi-step workflows and business processes.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"natural-language-processing-nlp\"\u003e\u003c/a\u003e\n\n## 📖 Natural Language Processing (NLP)\n\n\u003ca id=\"natural-language-processing-nlp-resources\"\u003e\u003c/a\u003e\n\n### Resources\n\nA selection of resources for learning and applying natural language processing in Python.\n\n- [Awesome Nlp](https://github.com/keon/awesome-nlp) - A ranked list of awesome Python libraries for natural language processing (NLP).\n- [Hugging Face NLP Course](https://huggingface.co/learn/llm-course/chapter1/1) - Official course on transformers and NLP from Hugging Face.\n- [Practical NLP Code](https://github.com/practical-nlp/practical-nlp-code) - Code examples and notebooks for practical natural language processing.\n- [Oxford Deep NLP Lectures](https://github.com/oxford-cs-deepnlp-2017/lectures) - Lecture materials from Oxford's Deep Natural Language Processing course.\n- [NLTK Book](https://www.nltk.org/book/) - Natural Language Processing with Python.\n- [NLP with Python by Susan Li](https://github.com/susanli2016/NLP-with-Python) - Jupyter notebooks demonstrating various NLP techniques and applications.\n- [Hands on NLTK Tutorial](https://github.com/hb20007/hands-on-nltk-tutorial) - The hands-on NLTK tutorial for NLP in Python.\n- [YSDA NLP Course](https://github.com/yandexdataschool/nlp_course) - Yandex School of Data Analysis course on Natural Language Processing.\n- [The NLP Pandect](https://github.com/ivan-bilan/The-NLP-Pandect) - Comprehensive NLP guide covering theory, models, and practical implementations.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"natural-language-processing-nlp-tools\"\u003e\u003c/a\u003e\n\n### Tools\n\nA collection of powerful libraries and frameworks for natural language processing.\n\n- [Natural Language Toolkit (NLTK)](https://www.nltk.org/) - A leading platform for building Python programs to work with human language data.\n- [TextBlob](https://textblob.readthedocs.io/en/dev/) - A simple library for processing textual data.\n- [SpaCy](https://spacy.io/) - An open-source software library for advanced NLP in Python.\n- [BERT](https://github.com/google-research/bert) - A transformer-based model for NLP tasks.\n- [Flair](https://github.com/flairNLP/flair) - A simple framework for state-of-the-art NLP.\n- [OpenHands](https://github.com/All-Hands-AI/OpenHands) - A library and framework for building applications with large language models.\n- [Stanford CoreNLP](https://github.com/stanfordnlp/CoreNLP) - A Java suite of core NLP tools providing fundamental linguistic analysis capabilities.\n- [John Snow Labs Spark-NLP](https://github.com/JohnSnowLabs/spark-nlp) - A state-of-the-art Natural Language Processing library built on Apache Spark.\n- [TextAttack](https://github.com/QData/TextAttack) - A Python framework for adversarial attacks, data augmentation, and model training in NLP.\n- [Gensim](https://github.com/piskvorky/gensim) - Topic modeling and natural language processing library for Python.\n- [Stanza](https://github.com/stanfordnlp/stanza) - Python NLP library for many human languages, from the Stanford NLP Group.\n- [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) - Framework for state-of-the-art sentence and text embeddings.\n- [LangExtract](https://github.com/google/langextract) - Google's library for structured information extraction from text using language models.\n- [Rasa](https://github.com/RasaHQ/rasa) - Open-source framework for building contextual AI assistants and chatbots.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"machine-learning\"\u003e\u003c/a\u003e\n\n## 🤖 Machine Learning \u0026 AI\n\n\u003ca id=\"machine-learning-resources\"\u003e\u003c/a\u003e\n\n### Resources\n\nA collection of resources to help you learn and apply machine learning concepts and techniques.\n\n- [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) - A curated list of awesome Machine Learning frameworks, libraries and software.\n- [Machine Learning Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials) - Machine learning and deep learning tutorials, articles and other resources.\n- [Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning) - A curated list of awesome Deep Learning tutorials, projects and communities.\n- [Best of ML Python](https://github.com/lukasmasuch/best-of-ml-python) - A ranked list of awesome machine learning Python libraries and tools.\n- [Microsoft ML for Beginners](https://github.com/microsoft/ML-For-Beginners) - A beginner-friendly introduction to machine learning concepts and practices.\n- [mlcourse.ai](https://github.com/Yorko/mlcourse.ai) - Open Machine Learning Course with practical assignments and real-world applications.\n- [Machine Learning Zoomcamp](https://github.com/DataTalksClub/machine-learning-zoomcamp) - A free practical machine learning course focused on building and deploying models.\n- [Awesome Artificial Intelligence](https://github.com/owainlewis/awesome-artificial-intelligence) - A curated list of artificial intelligence resources.\n- [Google Research](https://github.com/google-research/google-research) - Official repository for Google Research projects and publications.\n- [100 Days of ML Coding](https://github.com/Avik-Jain/100-Days-Of-ML-Code) - A comprehensive coding challenge to learn machine learning over 100 days.\n- [Made With ML](https://github.com/GokuMohandas/Made-With-ML) - Resource for building and deploying machine learning applications.\n- [Handson-ml3](https://github.com/ageron/handson-ml3) - Hands-on guide to machine learning and deep learning using Python.\n- [AI For Beginners](https://github.com/microsoft/AI-For-Beginners) - Microsoft's curriculum on artificial intelligence.\n- [LLMs-from-scratch](https://github.com/rasbt/LLMs-from-scratch) - Educational repository for building LLMs from scratch.\n- [Awesome Generative AI Guide](https://github.com/aishwaryanr/awesome-generative-ai-guide) - A comprehensive guide to generative AI models, tools, and applications.\n- [Awesome LLM](https://github.com/Hannibal046/Awesome-LLM) - A curated list of papers, projects, and resources related to Large Language Models.\n- [Machine Learning with Python by Susan Li](https://github.com/susanli2016/Machine-Learning-with-Python) - Jupyter notebooks covering various machine learning algorithms and applications.\n- [Understanding Deep Learning](https://udlbook.github.io/udlbook/) - Comprehensive and accessible textbook on deep learning fundamentals.\n- [Deep Learning Papers Reading Roadmap](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap) - Curated roadmap of seminal deep learning papers for newcomers.\n- [Applied ML](https://github.com/eugeneyan/applied-ml) - Curated resources and tools for applied machine learning in industry.\n- [Annotated deep learning paper implementations](https://github.com/labmlai/annotated_deep_learning_paper_implementations) - Implementations of deep learning papers with annotated code.\n- [Ml From Scratch](https://github.com/eriklindernoren/ML-From-Scratch) - Core machine learning algorithms implemented in Python from scratch.\n- [Awesome Ai Ml Resources](https://github.com/armankhondker/awesome-ai-ml-resources) - Carefully curated list of AI/ML books, courses, and practical tools.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"machine-learning-tools\"\u003e\u003c/a\u003e\n\n### Tools\n\nA collection of tools for developing and deploying machine learning models.\n\n#### Machine Learning\n\n- [Scikit-learn](https://github.com/scikit-learn/scikit-learn) - Machine learning library for classical algorithms and model building.\n- [XGBoost](https://github.com/dmlc/xgboost) - Optimized distributed gradient boosting library for tree-based models.\n- [LightGBM](https://github.com/microsoft/LightGBM) - Fast, distributed, high-performance gradient boosting framework.\n- [CatBoost](https://github.com/catboost/catboost) - High-performance gradient boosting on decision trees with categorical features support.\n- [H2O-3](https://github.com/h2oai/h2o-3) - Open-source distributed machine learning platform.\n- [cuML](https://github.com/rapidsai/cuml) - GPU-accelerated machine learning algorithms from RAPIDS.\n- [dlib](https://github.com/davisking/dlib) - Modern C++ toolkit containing machine learning algorithms and tools.\n- [SHAP](https://github.com/shap/shap) - Game theoretic approach to explain the output of any machine learning model.\n- [InterpretML](https://github.com/interpretml/interpret) - Fit interpretable models and explain blackbox machine learning.\n- [Optuna](https://github.com/optuna/optuna) - Hyperparameter optimization framework.\n\n#### Deep Learning\n\n- [TensorFlow](https://github.com/tensorflow/tensorflow) - End-to-end open source platform for machine learning and deep learning.\n- [PyTorch](https://github.com/pytorch/pytorch) - Deep learning framework with strong support for research and production.\n- [PyTorch Lightning](https://github.com/Lightning-AI/pytorch-lightning) - PyTorch wrapper for high-performance AI research.\n- [PyTorch Ignite](https://github.com/pytorch/ignite) - High-level library to help with training and evaluating neural networks.\n- [Keras](https://github.com/keras-team/keras) - High-level neural networks API, running on top of TensorFlow.\n- [Fast.ai](https://github.com/fastai/fastai) - Deep learning library simplifying training fast and accurate neural nets.\n- [HuggingFace Transformers](https://github.com/huggingface/transformers) - Model-definition framework for state-of-the-art machine learning models.\n- [HuggingFace Diffusers](https://github.com/huggingface/diffusers) - Library for state-of-the-art pretrained diffusion models.\n- [PEFT](https://github.com/huggingface/peft) - Library for efficiently adapting large pretrained models.\n- [YOLOv5](https://github.com/ultralytics/yolov5) - Real-time object detection system.\n- [Ultralytics](https://github.com/ultralytics/ultralytics) - YOLOv8 and other computer vision models.\n- [ONNX](https://github.com/onnx/onnx) - Open standard for machine learning interoperability.\n- [PyTorch Geometric](https://github.com/pyg-team/pytorch_geometric) - Geometric deep learning extension library for PyTorch.\n- [Pyro](https://github.com/pyro-ppl/pyro) - Deep universal probabilistic programming with Python and PyTorch.\n- [Skorch](https://github.com/skorch-dev/skorch) - Scikit-learn compatible neural network library.\n- [Sonnet](https://github.com/google-deepmind/sonnet) - DeepMind's library for building complex neural networks.\n- [JAX](https://github.com/jax-ml/jax) - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.\n- [TensorFlow Models](https://github.com/tensorflow/models) - Official TensorFlow repository with models and examples.\n- [Fenn](https://github.com/pyfenn/fenn) - A simple framework that automates ML/DL workflows by providing prebuilt trainers, templates, logging, configuration management, and much more.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"mlops\"\u003e\u003c/a\u003e\n\n## 🚀 MLOps\n\n\u003ca id=\"mlops-resources\"\u003e\u003c/a\u003e\n\n### Resources\n\nMaterials and curated lists for machine learning operations.\n\n- [MLOps Zoomcamp](https://github.com/DataTalksClub/mlops-zoomcamp) - A free course focused on the practical aspects of deploying and maintaining ML systems.\n- [Awesome MLOps (visenger)](https://github.com/visenger/awesome-mlops) - A curated list of references for MLOps.\n- [Awesome MLOps (kelvins)](https://github.com/kelvins/awesome-mlops) - A curated list of awesome MLOps tools.\n- [Awesome LLMOps](https://github.com/tensorchord/Awesome-LLMOps) - An awesome \u0026 curated list of best LLMOps tools for developers.\n- [LLM Zoomcamp](https://github.com/DataTalksClub/llm-zoomcamp) - A course dedicated to Large Language Models, their architecture and applications.\n- [ML Engineering Guide](https://github.com/stas00/ml-engineering) - A practical guide to machine learning engineering and MLOps best practices.\n- [Awesome Production Machine Learning](https://github.com/EthicalML/awesome-production-machine-learning) - A curated list of tools for deploying, monitoring, and maintaining ML systems in production.\n- [Llama Cookbook](https://github.com/meta-llama/llama-cookbook) - Official recipes and examples for working with Llama models.\n- [Awesome Kubeflow](https://github.com/terrytangyuan/awesome-kubeflow) - Curated resources, tools, and projects for the Kubeflow machine learning platform.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"mlops-tools\"\u003e\u003c/a\u003e\n\n### Tools\n\nPlatforms and utilities for deploying, monitoring, and maintaining ML systems.\n\n- [ColossalAI](https://github.com/hpcaitech/ColossalAI) - High-performance distributed training framework.\n- [DVC](https://github.com/iterative/dvc) - Version control system for machine learning projects.\n- [Evidently](https://github.com/evidentlyai/evidently) - Tool for analyzing and monitoring data and model drift.\n- [Deepchecks](https://github.com/deepchecks/deepchecks) - Validation for ML models and data.\n- [Sematic](https://github.com/sematic-ai/sematic) - Tool to build, debug, and execute ML pipelines with native Python.\n- [netdata](https://github.com/netdata/netdata) - Real-time performance monitoring.\n- [meilisearch](https://github.com/meilisearch/meilisearch) - Fast, open-source search engine.\n- [vLLM](https://github.com/vllm-project/vllm) - High-throughput and memory-efficient inference library for LLMs.\n- [haystack](https://github.com/deepset-ai/haystack) - LLM framework for building search and question answering systems.\n- [Kubeflow](https://github.com/kubeflow/kubeflow) - Machine learning toolkit for Kubernetes.\n- [Seldon Core](https://github.com/SeldonIO/seldon-core) - Open source platform for deploying and monitoring machine learning models in production.\n- [Feast](https://github.com/feast-dev/feast) - A feature store for machine learning that manages and serves ML features to models.\n- [BentoML](https://github.com/bentoml/BentoML) - Framework for building, shipping, and scaling ML applications.\n- [MLflow](https://github.com/mlflow/mlflow) - Open-source platform for the complete machine learning lifecycle.\n- [Wandb](https://github.com/wandb/wandb) - Tool for experiment tracking, dataset versioning, and model management.\n- [Comet ML](https://github.com/comet-ml/opik) - ML platform for tracking, comparing and optimizing experiments.\n- [Netflix Metaflow](https://github.com/Netflix/metaflow) - A human-friendly Python library for helping scientists and engineers build and manage real-life data science projects.\n- [mindsdb](https://github.com/mindsdb/mindsdb) - Platform for integrating AI into databases and applications.\n- [KServe](https://github.com/kserve/kserve) - Standardized serverless inference platform for deploying and serving machine learning models on Kubernetes.\n- [SQLFlow](https://github.com/sql-machine-learning/sqlflow) - Brings machine learning capabilities to SQL, enabling model training and prediction using SQL syntax.\n- [Jina AI Serve](https://github.com/jina-ai/serve) - Framework for building and deploying AI services that communicate via gRPC, HTTP and WebSockets.\n- [LiteLLM](https://github.com/BerriAI/litellm) - Unified interface to call all LLM APIs (OpenAI, Anthropic, Cohere, etc.) with consistent output formatting.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"ai-applications\"\u003e\u003c/a\u003e\n\n## 🧠 AI Applications \u0026 Platforms\n\n\u003ca id=\"ai-applications-resources\"\u003e\u003c/a\u003e\n\n### Resources\n\nA collection of resources focused on AI applications and platforms.\n\n- [Awesome LLM Apps](https://github.com/Shubhamsaboo/awesome-llm-apps) - Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.\n- [Awesome Generative AI](https://github.com/steven2358/awesome-generative-ai) - A curated list of modern Generative Artificial Intelligence projects and services.\n- [AI Agents for Beginners](https://github.com/microsoft/ai-agents-for-beginners) - Microsoft's course on designing and building AI agents.\n- [Generative AI for Beginners](https://github.com/microsoft/generative-ai-for-beginners) - Course on generative AI for beginners from Microsoft.\n- [Ai Dev Tools Zoomcamp](https://github.com/DataTalksClub/ai-dev-tools-zoomcamp) - Free hands-on course on modern tools for building and deploying AI applications.\n- [LLM Course](https://github.com/mlabonne/llm-course) - Practical course to master large language models from start to finish.\n- [Awesome AI Agents](https://github.com/e2b-dev/awesome-ai-agents) - A curated list of AI autonomous agents, environments, and frameworks.\n- [AI Collection](https://github.com/ai-collection/ai-collection) - The Generative AI Landscape - A Collection of Awesome Generative AI Applications.\n- [Awesome AI Apps](https://github.com/Arindam200/awesome-ai-apps) - A collection of projects showcasing RAG, agents, workflows, and other AI use cases.\n- [System Prompts and Models](https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools) - System Prompts, Internal Tools \u0026 AI Models from various AI applications and coding tools.\n- [RAG Techniques](https://github.com/NirDiamant/RAG_Techniques) - Collection of advanced techniques for Retrieval-Augmented Generation.\n- [Awesome LangChain](https://github.com/kyrolabs/awesome-langchain) - Awesome list of tools and projects with the awesome LangChain framework.\n- [Awesome AI Tools](https://github.com/mahseema/awesome-ai-tools) - A curated list of Artificial Intelligence Top Tools.\n- [Awesome LLM Security](https://github.com/corca-ai/awesome-llm-security) - A curation of awesome tools, documents and projects about LLM Security.\n- [Claude Cookbooks](https://github.com/anthropics/claude-cookbooks) - Official Anthropic examples and recipes for working with Claude AI.\n- [Hands On Large Language Models](https://github.com/HandsOnLLM/Hands-On-Large-Language-Models) - Covers LLM fundamentals, prompt engineering, and fine-tuning.  \n- [AI Engineering Hub](https://github.com/patchy631/ai-engineering-hub) - Resources for building, deploying, and maintaining AI systems.\n- [Agents Towards Production](https://github.com/NirDiamant/agents-towards-production) - Code-first tutorials for building production-grade GenAI agents.\n- [LLM Engineer Toolkit](https://github.com/KalyanKS-NLP/llm-engineer-toolkit) - Curated list of 120+ LLM libraries across various categories.\n- [GenAI Agents](https://github.com/NirDiamant/GenAI_Agents) - Repository of AI agent implementations and tutorials.  \n- [AI Notes](https://github.com/swyxio/ai-notes) - Personal notes and essays on AI and software development.\n- [Open LLMs](https://github.com/eugeneyan/open-llms) - Comprehensive list of open-source large language models and their capabilities.\n- [Prompt Engineering Guide](https://github.com/dair-ai/Prompt-Engineering-Guide) - Guides, papers, and resources for prompt engineering with LLMs.\n- [Prompt Engineering](https://github.com/NirDiamant/prompt_engineering) - Collection of prompt engineering techniques and strategies.\n- [500 AI Agents Projects](https://github.com/ashishpatel26/500-AI-Agents-Projects) - 500+ AI agent projects with code for learning and inspiration.\n- [Generative AI](https://github.com/genieincodebottle/generative-ai) - Roadmap and resources for mastering generative AI technologies.\n- [Awesome N8N](https://github.com/restyler/awesome-n8n) - Collection of templates, integrations, and resources for the n8n automation platform.\n- [Free Llm Api Resources](https://github.com/cheahjs/free-llm-api-resources) - Up-to-date list of free APIs for accessing large language models (LLMs).\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"ai-applications-tools\"\u003e\u003c/a\u003e\n\n### Tools\n\nA collection of frameworks, platforms, and end-user applications for building and deploying AI-powered solutions.\n\n#### AI Agents \u0026 Automation\n\n- [n8n](https://github.com/n8n-io/n8n) - Workflow automation platform for connecting APIs and services.\n- [crewAI](https://github.com/crewAIInc/crewAI) - Framework for orchestrating role-playing AI agents.\n- [autogen](https://github.com/microsoft/autogen) - Framework for building multi-agent conversational systems.\n- [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT) - Autonomous AI agent that can complete complex tasks.\n- [LangGraph](https://github.com/langchain-ai/langgraph) - Framework for building stateful, multi-actor applications with LLMs, with cycles and control flow.\n- [Agents.md](https://github.com/openai/agents.md) - Open source framework for building agentic AI systems.\nogrammatically.\n- [OpenManus](https://github.com/FoundationAgents/OpenManus) - Open-source platform for building and deploying AI agents.\n- [youtu-agent](https://github.com/TencentCloudADP/youtu-agent) - Multi-modal intelligent agent framework by Tencent Cloud.\n- [trae-agent](https://github.com/bytedance/trae-agent) - Tool-using reasoning agent with execution-augmented reasoning.\n- [deepagents](https://github.com/langchain-ai/deepagents) - LangChain framework for building sophisticated multi-agent systems.\n- [mem0](https://github.com/mem0ai/mem0) - AI memory system for long-term context and personalized interactions.\n- [web-ui](https://github.com/browser-use/web-ui) - AI-powered browser automation framework for web interaction.\n- [Agent-S](https://github.com/simular-ai/Agent-S) - Open agentic framework that autonomously interacts with computer GUIs like a human.\n- [Mastra](https://github.com/mastra-ai/mastra) - Open-source AI agent platform for building and scaling production-grade autonomous agents.\n- [Langflow](https://github.com/langflow-ai/langflow) - Powerful visual platform for building and deploying AI-powered agents and workflows.\n- [agenticSeek](https://github.com/Fosowl/agenticSeek) - Framework for building and deploying AI agents with advanced reasoning and tool use.\n- [Flowise](https://github.com/FlowiseAI/Flowise) - Open-source UI visual tool for building custom LLM orchestration flows and AI agents.\n- [MetaGPT](https://github.com/FoundationAgents/MetaGPT) - Multi-agent framework that simulates roles in a software company to build projects.\n- [Local Deep Research](https://github.com/LearningCircuit/local-deep-research) - Local AI research assistant that searches web, papers, and documents.\n- [Gptme](https://github.com/gptme/gptme) - AI agent CLI that writes code, uses terminal, browses web, and runs locally.\n- [Rowboat](https://github.com/rowboatlabs/rowboat) - Open-source AI coworker that learns from your emails/meetings to automate drafting, prep, and tasks.\n- [FutureSearch SDK](https://github.com/futuresearch/futuresearch-python) - Python SDK that dispatches parallel web-research agents across\n  table rows, synthesizing multi-agent findings into structured columns.\n- [Personal Ai Infrastructure](https://github.com/danielmiessler/Personal_AI_Infrastructure) - Framework for building a personal AI assistant with memory, skills, and learning ability.\n- [N8N Workflows](https://github.com/Zie619/n8n-workflows) - Collection of ready-to-use workflow templates for the n8n automation platform.\n- [Skyvern](https://github.com/Skyvern-AI/skyvern) - AI browser automation using LLMs \u0026 computer vision. Playwright-compatible SDK + no-code workflows.\n- [OpenWork](https://github.com/different-ai/openwork) - Open-source desktop alternative to Claude Cowork for running agents, skills, and MCP locally with team collaboration features.\n- [DeepAnalyze](https://github.com/ruc-datalab/DeepAnalyze) - Agentic LLM for autonomous data science, which can autonomously complete a wide range of data-centric tasks without human intervention.\n\n\n#### Development Frameworks \u0026 Tools\n\n- [LangChain](https://github.com/langchain-ai/langchain) - Framework for developing applications powered by language models.\n- [LlamaIndex](https://github.com/run-llama/llama_index) - Data framework for LLM-based applications with RAG capabilities.\n- [openai-python](https://github.com/openai/openai-python) - Official Python library for OpenAI API.\n- [openai-agents-python](https://github.com/openai/openai-agents-python) - Official OpenAI framework for building AI agents.\n- [ragflow](https://github.com/infiniflow/ragflow) - Open-source RAG (Retrieval-Augmented Generation) workflow platform.\n- [firecrawl](https://github.com/firecrawl/firecrawl) - Web crawling and data extraction service for AI applications.\n- [Fabric](https://github.com/danielmiessler/Fabric) - Framework for augmenting humans using AI.\n- [Dyad](https://github.com/dyad-sh/dyad) - Open-source platform for building AI applications with custom API keys.\n- [Langflow](https://github.com/langflow-ai/langflow) - Powerful visual platform for building and deploying AI-powered agents and workflows.\n- [NeMo](https://github.com/NVIDIA-NeMo/NeMo) - Scalable generative AI framework from NVIDIA for LLMs, Multimodal, and Speech AI.\n- [Deepcode](https://github.com/HKUDS/DeepCode) - AI-powered agent framework for automatic code generation from research papers and text.\n\n#### Code Generation \u0026 Assistance\n\n- [gpt-engineer](https://github.com/AntonOsika/gpt-engineer) - AI-powered code generation tool.\n- [gpt-pilot](https://github.com/Pythagora-io/gpt-pilot) - AI pair programmer that writes entire applications.\n- [tabby](https://github.com/TabbyML/tabby) - Self-hosted AI coding assistant.\n\n#### Model Deployment \u0026 Platforms\n\n- [Ollama](https://github.com/jmorganca/ollama) - Tool for running large language models locally.\n- [OpenLLM](https://github.com/bentoml/OpenLLM) - Open platform for operating large language models in production.\n- [LocalAI](https://github.com/mudler/LocalAI) - Self-hosted, local-first AI model deployment platform.\n- [dify](https://github.com/langgenius/dify) - Visual LLM application development platform.\n- [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) - Easy-to-use LLM fine-tuning framework.\n- [unsloth](https://github.com/unslothai/unsloth) - Library for faster and more memory-efficient LLM fine-tuning.\n- [LocalGPT](https://github.com/PromtEngineer/localGPT) - Fully private, on-premise document intelligence platform for chatting with your documents using local LLMs.\n\n#### AI Reliability \u0026 Debugging\n\n- [DeepEval](https://github.com/confident-ai/deepeval) - Pytest-style unit testing framework for LLMs. Metrics for RAG, agents, hallucination, summarization, and custom criteria.\n- [RAGAS](https://github.com/vibrantlabsai/ragas) - Evaluation toolkit for LLM apps. Metrics, test generation, and insights for optimizing RAG pipelines and agents.\n- [Phoenix](https://github.com/Arize-ai/phoenix) - AI observability platform. Tracing, datasets, experiments, and playground for troubleshooting and evaluating LLM apps.\n- [WFGY](https://github.com/onestardao/WFGY) - Open-source debugging infrastructure for RAG and AI agents. Includes 16-problem RAG failure map and TXT stress-test engine.\n\n#### End-User Applications\n\n- [open-webui](https://github.com/open-webui/open-webui) - Web interface for interacting with various LLMs.\n- [ComfyUI](https://github.com/comfyanonymous/ComfyUI) - Visual node-based interface for Stable Diffusion.\n- [lobe-chat](https://github.com/lobehub/lobe-chat) - Modern AI conversation interface.\n- [LibreChat](https://github.com/danny-avila/LibreChat) - Open-source ChatGPT alternative.\n- [quivr](https://github.com/QuivrHQ/quivr) - Personal second brain and AI assistant.\n- [upscayl](https://github.com/upscayl/upscayl) - AI-powered image upscaling tool.\n- [facefusion](https://github.com/facefusion/facefusion) - AI face swapping and enhancement tool.\n- [DocsGPT](https://github.com/arc53/DocsGPT) - Documentation-based question answering system.\n- [Deep Research](https://github.com/dzhng/deep-research) - AI-powered research assistant for iterative, deep research on any topic.\n- [Screenpipe](https://github.com/mediar-ai/screenpipe) - Local AI that records, searches, and automates tasks based on your screen and audio.\n- [Jaaz](https://github.com/11cafe/jaaz) - Open-source multimodal creative assistant and privacy-focused alternative to Canva/Manus for local image/video generation.\n- [DeepTutor](https://github.com/HKUDS/DeepTutor) - AI-powered personalized learning assistant with document Q\u0026A, exercise generation, and deep research capabilities.\n\n#### Additional Tools\n\n- [Bagel](https://github.com/ByteDance-Seed/Bagel) - Open-source unified multimodal model for understanding and generating images.\n- [Whisper](https://github.com/openai/whisper) - Robust speech recognition model for transcription and translation.\n- [ChatTTS](https://github.com/2noise/ChatTTS) - Generative TTS model optimized for natural, expressive daily dialogue with fine-grained prosody control.\n- [NeuTTS](https://github.com/neuphonic/neutts) - On-device TTS model with instant voice cloning from audio samples..\n- [Everything Claude Code](https://github.com/affaan-m/everything-claude-code) - Collection of resources, guides, and tools for effective Claude Code AI assistant use.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"cloud-platforms\"\u003e\u003c/a\u003e\n\n## ☁️ Cloud Platforms \u0026 Infrastructure\n\n\u003ca id=\"cloud-platform-resources\"\u003e\u003c/a\u003e\n\n### Resources\n\nA collection of resources for mastering cloud-native technologies, containerization, and infrastructure management.\n\n- [Awesome Cloud Native](https://github.com/rootsongjc/awesome-cloud-native) - A curated list of resources for cloud native technologies.\n- [Awesome Kubernetes](https://github.com/ramitsurana/awesome-kubernetes) - A curated list for awesome Kubernetes resources.\n- [Awesome Docker](https://github.com/veggiemonk/awesome-docker) - A curated list of Docker resources and projects.\n- [AWS Well-Architected Labs](https://github.com/awslabs/aws-well-architected-labs) - Hands-on labs to help you learn about the AWS Well-Architected Framework.\n- [Kubernetes The Hard Way](https://github.com/kelseyhightower/kubernetes-the-hard-way) - Tutorial for bootstrapping a Kubernetes cluster the hard way on Google Cloud Platform.\n- [Awesome Compose](https://github.com/docker/awesome-compose) - A curated list of Docker Compose samples.\n- [AWS EKS Best Practices](https://github.com/aws/aws-eks-best-practices) - A best practices guide for Amazon EKS.\n- [Awesome Selfhosted](https://github.com/awesome-selfhosted/awesome-selfhosted) - A list of Free Software network services and web applications which can be hosted locally.\n- [Awesome Selfhosted Docker](https://github.com/hotheadhacker/awesome-selfhost-docker) - A curated list of awesome selfhosted applications and solutions using Docker.\n- [Awesome Kubernetes Resources](https://github.com/tomhuang12/awesome-k8s-resources) - A curated list of awesome Kubernetes tutorials, tools, and resources.\n- [Awesome Cloud Security](https://github.com/4ndersonLin/awesome-cloud-security) - A curated list of awesome cloud security resources, tools, and best practices.\n- [DevOps Exercises](https://github.com/bregman-arie/devops-exercises) - Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, and more.\n- [Awesome Cloudsec Labs](https://github.com/iknowjason/Awesome-CloudSec-Labs) - Curated hands-on labs and exercises for learning cloud security platforms.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"cloud-platform-tools\"\u003e\u003c/a\u003e\n\n### Tools\n\nTools for containerization, orchestration, infrastructure as code, and cloud-native development.\n\n#### Containerization \u0026 Orchestration\n\n- [Docker](https://github.com/docker) - Open platform for developing, shipping, and running applications in containers.\n- [Docker Compose](https://github.com/docker/compose) - A tool for defining and running multi-container Docker applications.\n- [Kubernetes](https://github.com/kubernetes/kubernetes) - Production-grade container orchestration system.\n- [Kompose](https://github.com/kubernetes/kompose) - Conversion tool from Docker Compose to Kubernetes.\n\n#### Infrastructure as Code\n\n- [Terraform](https://github.com/hashicorp/terraform) - Infrastructure as Code tool.\n- [OpenTofu](https://github.com/opentofu/opentofu) - Open source fork of Terraform.\n- [Pulumi](https://github.com/pulumi/pulumi) - Modern IaC platform using familiar programming languages.\n- [CDK8s](https://github.com/cdk8s-team/cdk8s) - Define Kubernetes apps using familiar languages.\n\n#### CI/CD \u0026 GitOps\n\n- [Jenkins](https://github.com/jenkinsci/jenkins) - Open source automation server.\n- [Argo CD](https://github.com/argoproj/argo-cd) - Declarative GitOps continuous delivery.\n- [Argo Workflows](https://github.com/argoproj/argo-workflows) - Container-native workflow engine.\n- [Tekton](https://github.com/tektoncd/pipeline) - Kubernetes-native CI/CD framework.\n- [Spinnaker](https://github.com/spinnaker/spinnaker) - Multi-cloud continuous delivery.\n- [Dagger](https://github.com/dagger/dagger) - Portable devkit for CI/CD pipelines.\n\n#### Service Mesh \u0026 API Gateways\n\n- [Traefik](https://github.com/traefik/traefik) - Modern HTTP reverse proxy and load balancer.\n- [Kong](https://github.com/Kong/kong) - Cloud-native API Gateway.\n- [Apache APISIX](https://github.com/apache/apisix) - Dynamic API gateway.\n- [Envoy Gateway](https://github.com/envoyproxy/gateway) - Manages Envoy Proxy as gateway.\n- [Higress](https://github.com/alibaba/higress) - Cloud-native API gateway based on Istio.\n- [Meshery](https://github.com/meshery/meshery) - Service mesh management.\n\n#### Kubernetes Ecosystem\n\n- [Helm](https://github.com/helm/helm) - Package manager for Kubernetes.\n- [Kustomize](https://github.com/kubernetes-sigs/kustomize) - Configuration customization for Kubernetes.\n- [Kubernetes Dashboard](https://github.com/kubernetes/dashboard) - Web-based UI for Kubernetes.\n- [Skaffold](https://github.com/GoogleContainerTools/skaffold) - Continuous development for Kubernetes.\n- [Tilt](https://github.com/tilt-dev/tilt) - Local development for Kubernetes.\n- [Flagger](https://github.com/fluxcd/flagger) - Progressive delivery operator.\n- [KubeVela](https://github.com/kubevela/kubevela) - Application delivery platform.\n- [KubeSphere](https://github.com/kubesphere/kubesphere) - Kubernetes multi-cloud management.\n\n#### Developer Platforms \u0026 Control Planes\n\n- [Crossplane](https://github.com/crossplane/crossplane) - Cloud native control plane.\n- [Artifact Hub](https://github.com/artifacthub/hub) - Kubernetes packages and Helm charts.\n- [Devtron](https://github.com/devtron-labs/devtron) - Kubernetes dashboard.\n- [Harness](https://github.com/harness/harness) - End-to-end developer platform.\n\n#### Additional Tools\n\n- [Vagrant](https://github.com/hashicorp/vagrant) - Tool for building and managing portable virtual development environments as code.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"system-design\"\u003e\u003c/a\u003e\n\n## 🏗️ System Design \u0026 Architecture\n\nResources for building and architecting robust, scalable data systems.\n\n- [System Design Primer](https://github.com/donnemartin/system-design-primer) - Comprehensive guide to large-scale system design. Includes Anki flashcards for interview prep.\n- [Awesome Scalability](https://github.com/binhnguyennus/awesome-scalability) - Design patterns for building scalable systems with real-world case studies from Netflix, Facebook, and Uber.\n- [System Design 101](https://github.com/ByteByteGoHq/system-design-101) - Visual introduction to system design concepts with detailed diagrams and illustrations.\n- [Awesome System Design Resources](https://github.com/ashishps1/awesome-system-design-resources) - Curated collection of articles, videos, books, and interview questions.\n- [System Design](https://github.com/karanpratapsingh/system-design) - Concise course covering load balancing, caching, databases, and CAP theorem.\n- [System Design Academy](https://github.com/systemdesign42/system-design-academy) - Practical system design examples and structured interview preparation.\n- [Awesome Software Architecture](https://github.com/mehdihadeli/awesome-software-architecture) - Extensive catalog of software architecture articles, books, and tools.\n- [Awesome Design Patterns](https://github.com/DovAmir/awesome-design-patterns) - Collection of patterns from code-level to microservices and big data.\n- [System Design Resources](https://github.com/InterviewReady/system-design-resources) - Practical questions, diagrams, and solutions for architecture interviews.\n- [Domain-Driven Hexagon](https://github.com/Sairyss/domain-driven-hexagon) - Deep dive into Domain-Driven Design (DDD) and hexagonal architecture.\n- [Awesome Design Systems](https://github.com/alexpate/awesome-design-systems) - Collection of design system guides and component libraries for large projects.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"productivity\"\u003e\u003c/a\u003e\n\n## ⚡ Productivity\n\n\u003ca id=\"productivity-resources\"\u003e\u003c/a\u003e\n\n### Resources\n\nA collection of resources to enhance productivity.\n\n- [Positron](https://github.com/posit-dev/positron) - A next-generation data science IDE.\n- [Nanobrowser](https://github.com/nanobrowser/nanobrowser) - An open-source AI web automation tool with multi-agent system that runs directly in your browser.\n- [Best of Jupyter](https://github.com/ml-tooling/best-of-jupyter) - Ranked list of notable Jupyter Notebook, Hub, and Lab projects.\n- [Deepnote](https://github.com/deepnote/deepnote) - AI native data science notebook platform compatible with Jupyter, featuring real-time collaboration, environment management, and integrations.\n- [AFFiNE](https://github.com/toeverything/AFFiNE) - All-in-one workspace for notes, docs, and data visualization.\n- [Marimo](https://github.com/marimo-team/marimo) - Reactive Python notebook for reproducible and interactive data science.\n- [ChatGPT Data Science Prompts](https://github.com/travistangvh/ChatGPT-Data-Science-Prompts) - A collection of useful prompts for data scientists using ChatGPT.\n- [Gamma.app](https://gamma.app/) - AI-powered platform for creating and sharing presentations and documents.\n- [Cookiecutter Data Science](https://github.com/drivendataorg/cookiecutter-data-science) - A standardized project structure for data science projects.\n- [Learn Regex](https://github.com/ziishaned/learn-regex) - Comprehensive guide to learning regular expressions with examples and exercises.\n- [Awesome Regex](https://github.com/aloisdg/awesome-regex) - Curated collection of regex tools, libraries, and learning resources.\n- [The Markdown Guide](https://www.markdownguide.org/) - Comprehensive guide to learning Markdown.\n- [Readme-AI](https://github.com/eli64s/readme-ai) - A tool to automatically generate README.md files for your projects.\n- [Markdown Here](https://github.com/adam-p/markdown-here) - Extension for writing emails in Markdown and rendering them before sending.\n- [MarkText](https://github.com/marktext/marktext) - Simple and elegant markdown editor for documentation.\n- [QuarkDown](https://github.com/iamgio/quarkdown) - Lightweight markdown processor for fast document rendering.\n- [screenshot-to-code](https://github.com/abi/screenshot-to-code) - AI tool that converts screenshots into code for various frontend stacks.\n- [Codebeautify](https://codebeautify.org/) - All-in-one online code formatter and beautifier for Python, SQL, JSON, and more.\n- [Notion](https://www.notion.com/) - An all-in-one workspace for note-taking and task management.\n- [Trello](https://trello.com/home) - A visual project management tool.\n- [Habitica](https://github.com/HabitRPG/habitica) - A habit-building and productivity app that treats your life like a role-playing game.\n- [Bujo](https://bulletjournal.com/) - Tools to help transform the way you work and live.\n- [Parabola](https://parabola.io/) - An AI-powered workflow builder for organizing data.\n- [Asana](https://asana.com/) - A project management platform for tracking work and projects.\n- [Puter](https://github.com/HeyPuter/puter) - An open-source, browser-based computing environment and cloud OS.\n- [Milkdown](https://github.com/Milkdown/milkdown) - Plugin-driven, WYSIWYG markdown editor framework inspired by Typora.\n- [PDFMathTranslate](https://github.com/PDFMathTranslate/PDFMathTranslate) - AI tool for bilingual scientific PDF translation preserving formulas, charts, and layout.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"productivity-useful-linux-tools\"\u003e\u003c/a\u003e\n\n### Useful Linux Tools\n\nA selection of tools to enhance productivity and functionality in Linux environments.\n\n- [tldr-pages](https://github.com/tldr-pages/tldr) - Simplified and community-driven man pages with practical examples.\n- [Bat](https://github.com/sharkdp/bat) - Cat clone with syntax highlighting.\n- [Exa](https://github.com/ogham/exa) - Modern replacement for ls.\n- [Ripgrep](https://github.com/BurntSushi/ripgrep) - Faster grep alternative.\n- [Zoxide](https://github.com/ajeetdsouza/zoxide) - Smarter cd command.\n- [Peek](https://github.com/phw/peek) - Simple animated GIF screen recorder with an easy to use interface.\n- [CopyQ](https://github.com/hluk/CopyQ) - Clipboard manager with advanced features.\n- [Translate Shell](https://github.com/soimort/translate-shell) - Command-line translator using Google Translate, Bing Translator, Yandex.Translate, etc.\n- [Espanso](https://github.com/espanso/espanso) - Cross-platform Text Expander written in Rust.\n- [Flameshot](https://github.com/flameshot-org/flameshot) - Powerful yet simple to use screenshot software.\n- [DrawIO Desktop](https://github.com/jgraph/drawio-desktop) - An open-source diagramming software for making flowcharts, process diagrams, and more.\n- [Inkscape](https://github.com/inkscape/inkscape) - A powerful, free, and open-source vector graphics editor for creating and editing visualizations.\n- [Rclone](https://rclone.org/) - A command-line program to manage files on cloud storage.\n- [Rsync](https://rsync.samba.org/) - A fast and versatile file copying tool that can synchronize files and directories between two locations over a network or locally.\n- [Timeshift](https://github.com/linuxmint/timeshift) - System restore tool for Linux that creates filesystem snapshots using rsync+hardlinks or BTRFS snapshots.\n- [Backintime](https://github.com/bit-team/backintime) - A comfortable and well-configurable graphical frontend for incremental backups.\n- [Fzf](https://github.com/junegunn/fzf) - A command-line fuzzy finder.\n- [Osquery](https://github.com/osquery/osquery) - SQL powered operating system instrumentation, monitoring, and analytics.\n- [GNU Parallel](https://www.gnu.org/software/parallel/) - A tool to run jobs in parallel.\n- [HTop](https://htop.dev/) - An interactive process viewer.\n- [Ncdu](https://dev.yorhel.nl/ncdu) - A disk usage analyzer with an ncurses interface.\n- [Thefuck](https://github.com/nvbn/thefuck) - A command line tool to correct your previous console command.\n- [Miller](https://github.com/johnkerl/miller) - A tool for querying, processing, and formatting data in various file formats (CSV, JSON, etc.), like awk/sed/cut for data.\n- [jq](https://github.com/jqlang/jq) - Command-line JSON processor for parsing and manipulating JSON data.\n- [yq](https://github.com/mikefarah/yq) - Portable command-line YAML processor (like jq for YAML and XML).\n- [q](https://github.com/harelba/q) - Run SQL directly on CSV or TSV files from the command line.\n- [VisiData](https://github.com/saulpw/visidata) - Interactive multitool for tabular data exploration in the terminal.\n- [csvkit](https://github.com/wireservice/csvkit) - Suite of command-line tools for working with CSV data.\n- [httpie](https://github.com/httpie/cli) - Modern command-line HTTP client for API testing and debugging.\n- [glances](https://github.com/nicolargo/glances) - Cross-platform system monitoring tool for resource usage analysis.\n- [hyperfine](https://github.com/sharkdp/hyperfine) - Command-line benchmarking tool for performance testing.\n- [termgraph](https://github.com/mkaz/termgraph) - Draw basic graphs in the terminal for quick data visualization.  \n- [fd](https://github.com/sharkdp/fd) - Simple, fast and user-friendly alternative to 'find'.\n- [dust](https://github.com/bootandy/dust) - More intuitive version of du written in rust.\n- [bottom](https://github.com/ClementTsang/bottom) - Cross-platform graphical process/system monitor.\n- [Keychain](https://github.com/danielrobbins/keychain) - Tool for managing and securely storing passwords and secrets.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003ca id=\"productivity-useful-vs-code-extensions\"\u003e\u003c/a\u003e\n\n### Useful VS Code Extensions\n\nA collection of extensions to enhance functionality and productivity in Visual Studio Code.\n\n- [JDBC Adapter](https://marketplace.visualstudio.com/items/?itemName=cweijan.dbclient-jdbc) - Connect to various databases using JDBC.\n- [DBCode - Connect](https://marketplace.visualstudio.com/items?itemName=DBCode.dbcode) - Database client for managing and querying databases.\n- [Markdown All in One](https://marketplace.visualstudio.com/items?itemName=yzhang.markdown-all-in-one) - Essential tools for Markdown editing.\n- [Markdown Preview GitHub Styles](https://marketplace.visualstudio.com/items?itemName=bierner.markdown-preview-github-styles) - Changes VS Code's markdown preview to match GitHub's styling.\n- [Snippington Python Pandas Basic](https://marketplace.visualstudio.com/items?itemName=snippington.snp-pandas-basic) - Basic tools for working with Pandas in Python.\n- [PDF Viewer for Visual Studio Code](https://marketplace.visualstudio.com/items?itemName=mathematic.vscode-pdf) - View PDF files directly in VS Code.\n- [Quick Python Print](https://marketplace.visualstudio.com/items?itemName=WeidaWang.quick-python-print) - Quickly handle print operations in Python.\n- [Rainbow CSV](https://marketplace.visualstudio.com/items?itemName=mechatroner.rainbow-csv) - Highlight CSV and TSV files and run SQL-like queries.\n- [Remove Blank Lines](https://marketplace.visualstudio.com/items?itemName=thamaraiselvam.remove-blank-lines) - Extension to remove empty lines in documents.\n- [PDF Preview in VSCode](https://marketplace.visualstudio.com/items/tomoki1207.pdf) - Show PDF previews in VS Code.\n- [CSV to Table](https://marketplace.visualstudio.com/items?itemName=phplasma.csv-to-table) - Convert CSV/TSV/PSV files to ASCII formatted tables.\n- [Data Preview](https://marketplace.visualstudio.com/items?itemName=RandomFractalsInc.vscode-data-preview) - Import, view, slice, and export data.\n- [Data Wrangler](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.datawrangler) - Tool for cleaning and preparing tabular datasets.\n- [Error Lens](https://marketp","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/pavelgrigoryevds%2Fawesome-data-analysis/projects"}