This curated collection of awesome repositories on GitHub is a comprehensive guide for developers, aspiring programmers, and anyone interested in exploring the vast world of open-source software. The list covers programming languages, frameworks, data science, DevOps, and more, offering valuable resources for various stages of development and learning. Theer are list of courses and practices site also included.
- Awesome JavaScript: A comprehensive list of JavaScript frameworks, libraries, tools, and resources.
- Awesome React: Collection of React frameworks, libraries, tools, and resources.
- Awesome Vue: Curated list of Vue.js resources, libraries, tools, and frameworks.
- Awesome Svelte: Compilation of Svelte resources, libraries, tools, and frameworks.
- Awesome Nuxt: Dedicated list for Nuxt.js resources, libraries, tools, and frameworks.
- Awesome Next.js: Comprehensive resource list for Next.js libraries, tools, and frameworks.
- Awesome Svelte Kit: Curated resources for Svelte Kit, including libraries, tools, and frameworks.
- Awesome Remix: Collection of resources for Remix, encompassing libraries, tools, and frameworks.
- Awesome Astro: Curated list of Astro resources, libraries, tools, and frameworks.
- JavaScript Algorithms by trekhleb: Contains JavaScript-based examples of many popular algorithms and data structures. Great for JavaScript developers and computer science students.
- Awesome Python: Extensive list of Python libraries, frameworks, and resources.
- Awesome Data Science: Collection of data science frameworks, libraries, resources, and tools.
- Awesome Data Science resources: Comprehensive list for machine learning frameworks, libraries, and resources.
- Awesome TensorFlow: Curated TensorFlow tutorials, libraries, and resources.
- Awesome PyTorch: Collection of PyTorch frameworks, libraries, and resources.
- Ollama by jmorganca: An innovative project focused on machine learning and AI. Perfect for those interested in cutting-edge AI developments.
- Awesome Machine Learning: Curated resources for machine learning, including frameworks and libraries.
- Awesome SQL: Curated list of SQL tools, libraries, resources, and tutorials.
- Awesome Databases learning: Comprehensive list of database technologies and papers.
- Awesome NoSQL: Curated list of NoSQL Resources
- Awesome Public Datasets: Topic-centric list of public dataasets
- Public APIs by marcelscruz: A collective list of free APIs for development. Beneficial for software developers and API integrators.
- Awesome Front-End: Collection of front-end frameworks, libraries, and resources.
- Awesome React Native: Curated list of React Native components, tools, and resources.
- Awesome Flutter: Extensive resources for Flutter, including libraries, tools, and tutorials.
- Awesome Node.js: Comprehensive resources for Node.js frameworks, libraries, and tools.
- Awesome Laravel: Curated Laravel packages, resources, and opinions.
- Awesome Ruby on Rails: Best practices and coding conventions for Ruby on Rails framework.
- Awesome Django: Collection of Django frameworks, libraries, and resources.
- Awesome Flask: Curated Flask frameworks, libraries, and resources.
- Awesome Go: Comprehensive list of Go frameworks, libraries, software, and resources.
- Awesome Rust: Curated projects and learning resources focused on Rust.
- Awesome Spring: Collection of Spring Framework libraries, tools, and resources.
- Design Resources for Developers by bradtraversy: A comprehensive list of design and UI resources for developers. Useful for front-end developers and web designers.
- Awesome CSS: Curated list of CSS resources
- Codewars: Online code challenges for programming skill development.
- LeetCode: Platform with a vast library of programming problems for practice.
- W3Schools: Comprehensive resource for learning web development languages and technologies.
- FreeCodeCamp: Free coding bootcamp with interactive tutorials and exercises.
- freeCodeCamp by freeCodeCamp: An open-source community that helps you learn to code. Perfect for beginners and those looking to strengthen their programming skills.
- FullStackOpen: Open-source Full-Stack Web Development Course from University of Helsinki and Houston.
- Mooc.fi: Finnish MOOC platform offering courses in programming, Cybersecurity and data science in English and Finnish.
- Best Websites for Programmers by sdmg15: An essential list of websites and resources for programmers. Useful for all levels of software developers and computer science students.
- Web Development for Beginners by Microsoft: A 24-lesson curriculum all about JavaScript, CSS, and HTML basics. Ideal for those starting in web development.
- The Odin Project: Structured curriculum for learning web development.
- Dataquest.io: Courses on data science topics, including SQL, Python, and Machine Learning.
- DataCamp: Interactive courses on data science with hands-on projects and community support.
- SoloLearn: Coding courses for beginners and experienced developers.
- Mimo: Interactive coding lessons in a gamified mobile app format.
- Py4e.com: Interactive Python tutorials covering the basics of the language.
- Dj4e.com: Interactive Python tutorials on data visualization using Matplotlib.
- Pg4e.com: Tutorials on working with PostgreSQL databases.
- Kaggle: Platform for data scientists with machine learning competitions and shared resources.
- Awesome Bash: Curated list for bash scripting
- Awesome PowerShell: Curated list of Awesome PowerShell resources
- Awesome Pentest by enaqx: A collection of penetration testing resources, tools, and more. Essential for cybersecurity professionals and penetration testers.
- Awesome Cybersecurity by liyansong2018: A curated collection of cybersecurity resources. Great for anyone in the cybersecurity field, including security analysts and ethical hackers.
- PicoCTF: Online CTF game for learning cybersecurity through challenges.
- TryHackMe: Cybersecurity challenges with tutorials, walkthroughs, and lab environments.
- PortSwigger Academy: Courses on web application security from the creators of Burp Suite.
- PentesterLab: Penetration testing environments for learning cybersecurity skills.
- HackTheBox: Penetration testing challenges and labs for skill development.
- Haiku: Interactive walkthroughs for learning cybersecurity concepts.
- LetsDefend.io: Cybersecurity training with tutorials, exercises, and community forums.
- Hacker101: Comprehensive curriculum for learning cybersecurity skills.
- Certified in Cybersecurity: CC Entry level Cybersecurity Certificate free for the first 1Million participants.
- HackerOne: Platform connecting security researchers with companies for vulnerability reporting.
- Intigriti: Bug bounty programs and security services for companies.
- BugCrowd: Platform for cybersecurity professionals to find and report vulnerabilities.
- Shodan: A search engine for Internet-connected devices. It's an invaluable tool for network security professionals and cybersecurity researchers, helping to discover which of your devices are connected to the internet, where they are located, and who is using them.
- 30 Seconds of Code by Chalarangelo: A collection of short code snippets for all your development needs. Ideal for quick learning and reference for developers.
- Projects by karan: A list of practical projects that anyone can solve in any programming language. Suitable for developers looking to improve their coding skills.
- Build Your Own X by codecrafters-io: Encourages you to learn by doing, by building clones of popular technologies. Fantastic for hands-on learners and developers looking to understand how things work.
- Threestudio by threestudio-project: A creative tool for 3D design and animation. Great for designers and developers in game development and 3D modeling.
- SQLFiddle: Interactive tool for experimenting with SQL queries.
- GetSQLPad: Collaborative environment for practicing SQL queries.
- SQLZoo: Collection of SQL practice problems.
- db-fiddle: Tool for experimenting with SQL queries.
- livesql.oracle.com/: Oracle's database for practicing SQL queries.
- sql-playground.wizardzines.com/: Interactive tool for SQL query experimentation.
- sandboxsql.com/: Cloud-based SQL database for practice and experimentation.
- Awesome Docker by veggiemonk: A curated list of Docker resources and projects. Ideal for DevOps engineers and cloud computing professionals.
- Awesome K8s Resources by tomhuang12: An extensive collection of Kubernetes resources and tools. Great for system administrators and engineers working in container orchestration.
- System Design Primer by donnemartin: A repository to learn about system design. Invaluable for software engineers and system architects.
- Khan Academy - Statistics and Probability: Ideal for data analysts and data scientists, this course covers foundational concepts in statistics and probability.
- Khan Academy - Probability: Essential for software engineers and AI specialists, focusing on probability theories and applications.
- Khan Academy - AP Statistics: Great for aspiring data scientists, offering advanced statistics concepts used in data analysis.
- Khan Academy - Precalculus: Useful for developers and engineers, providing a strong mathematical foundation.
- Khan Academy - Differential Calculus: Important for AI and machine learning professionals, covering the basics of differential calculus.
- Khan Academy - Integral Calculus: Key for those in scientific computing and algorithm development, focusing on integral calculus.
- Khan Academy - Calculus 1: Essential for programmers and analysts, offering an introduction to calculus.
- Khan Academy - Calculus 2: Advanced calculus topics beneficial for machine learning and complex algorithm developers.
- Khan Academy - AP Calculus AB: Useful for software developers, covering first-semester college calculus.
- Khan Academy - AP Calculus BC: Second-semester college calculus, important for engineers and AI professionals.
- Khan Academy - Linear Algebra: Critical for machine learning and data science, focusing on linear algebra concepts.
- Coursera - Mathematics for Machine Learning and Data Science Specialization: A comprehensive series for data scientists and AI practitioners, covering essential mathematical concepts for machine learning and data science.
- Coursera - Machine Learning: Probability and Statistics: Focused on probability and statistics in machine learning, this course is vital for machine learning engineers and data analysts.
- Coursera - Machine Learning: Calculus: Designed for machine learning enthusiasts, it delves into the calculus concepts used in machine learning algorithms.
- Coursera - Machine Learning: Linear Algebra: Essential for anyone in AI and machine learning, focusing on linear algebra and its applications in machine learning models.
- Roadmap.sh: Multiple language and techfield based roadmaps.
- Data Science Master by datasciencemasters: An open-source data science curriculum. Excellent for aspiring data scientists and analysts.
- Roadmap To Learn Generative AI In 2024 by krishnaik06: A comprehensive roadmap for learning Generative AI, ideal for AI enthusiasts and professionals looking to specialize in generative models.
If you have any question about this collection of links, do not hesitate to contact me @TimoCodes on X or open an issue on GitHub.
See the separate file Contribution Guidelines