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Top 20 GitHub Repositories for Data Science: This repository curates the top resources available on GitHub for those interested in the field of data science. From cheat sheets to full projects, these resources provide a wide range of materials to learn and practice data science skills.
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Free Courses for Data Science and Data Analytics: In addition to GitHub resources, a list of free courses is included for those interested in learning data science and data analytics. These courses, offered by Microsoft, Harvard, Stanford, and others, cover a variety of essential topics to master the field.
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Free Courses for Artificial Intelligence: This repository not only focuses on data science and data analytics but also offers a wide selection of free courses in artificial intelligence. From introductory courses to more advanced materials, these resources are perfect for those looking to explore the exciting field of artificial intelligence.
With this repository, we aim to provide a comprehensive and accessible guide for anyone to kickstart their journey in data science, data analytics, and artificial intelligence for free. We hope you find these resources helpful and enriching for your learning!
Don't forget to bookmark this repository for easy access! ⭐️
| # | Repository Name | Link |
|---|---|---|
| 1 | Data Scientist Roadmap | Data Scientist Roadmap |
| 2 | Learn Data Science | Learn Data Science |
| 3 | Awesome Python | Awesome Python |
| 4 | Data Science in Python | Data Science in Python |
| 5 | DS Python Notebooks | DS Python Notebooks |
| 6 | Awesome Data Science | Awesome Data Science |
| 7 | Self taught DS | Self taught DS |
| 8 | Time Series Best | Time Series Best |
| 9 | Master Data Science | Master Data Science |
| 10 | Best Keras resources | Best Keras resources |
| 11 | Pandas exercise | Pandas exercise |
| 12 | Keras resources | Keras resources |
| 13 | Best DS resources | Best DS resources |
| 14 | Become a Data Engineer | Become a Data Engineer |
| 15 | Awesome DE | Awesome DE |
| 16 | Data Science Cheatsheets | Data Science Cheatsheets |
| 17 | 1000+ Data Science blogs | 1000+ Data Science blogs |
| 18 | Free Data Science Books | Free Data Science Books |
| 19 | Data Science QnAs | Data Science QnAs |
| 20 | Data Science interviews | Data Science interviews |
| No. | Course Title | Link |
|---|---|---|
| 1 | Get started with Microsoft data analytics | Link |
| 2 | Prepare data for analysis with Power BI | Link |
| 3 | Model data with Power BI | Link |
| 4 | Build Power BI visuals and reports | Link |
| 5 | Python | Link |
| 6 | SQL | Link |
| 7 | R | Link |
| 8 | Excel, PowerBI | Link |
| 9 | PowerBI (Data Analytics) | Link |
| 10 | Microsoft Certified: Power BI Data Analyst Associate – Certifications | Link |
| 11 | Use Power Bi for Financial Data Analysis | Link |
| 12 | Data-Driven Decisions with Power BI | Link |
| 13 | Data Modeling in Power BI | Link |
| 14 | Microsoft Power BI Data Analyst Professional Certificate | Link |
| 15 | Power Bi Report Development Crash Course | Link |
| No. | Título | Link |
|---|---|---|
| 1 | Generative AI by Microsoft | link |
| 2 | MIT Efficient DL Computing | link |
| 3 | NLP by UT Austin | link |
| 4 | Deep Learning by Sebastian | link |
| 5 | LLMs Bootcamp | link |
| 6 | Harvard Intro to Python | link |
| 7 | Deep Learning for Coders | link |
| 8 | MIT Intro to Deep Learning | link |
| 9 | Neural Networks by Karpathy | link |
| 10 | Prompt Engineering Guide | link |
| 11 | Computer Vision Practice | link |
| 12 | Advanced NLP | link |
| 13 | ML Stanford University | link |
| 14 | ML Engineering Andrew Ng | link |
| 15 | Multimodal ML Carnegie | link |
| 16 | Stanford Deep Meta | link |
| 17 | Stanford NLU | link |
| 18 | Stanford Transformers | link |
| 19 | DL by Yann LeCun | link |
| 20 | NLP by Hugging Face | link |
| 21 | Deep Learning by d2l_ai | link |
| 22 | Deepmind × UCL RL | link |
| 23 | Stanford NLP + DL | link |
| 24 | Deepmind × UCL DL | link |
| 25 | Advanced Linear Algebra | link |
| 26 | Stanford AI Techniques | link |
| 27 | Practical DL | link |
| 28 | UMich DL for CV | link |
| 29 | Harvard Stats Probability | link |