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Learning data science through courses or YouTube videos can get monotonous as it often involves passive consumption of information. You’re not getting your hands dirty, experimenting, or building anything. You’re simply absorbing content from a screen. But what if I told you that there was a more engaging and effective way to understand data science tools and concepts? That’s right. Today, we’re going to explore 10 GitHub repositories that will help you master data science concepts through interactive courses, books, guides, code samples, projects, free courses based on top college curricula, interview questions, and best practices.
1. Virgilio: your mentor in data science
Repository: virgili0/Virgil
Virgilio is a comprehensive guide and mentor for data science e-learning. It offers structured content, tutorials, and resources to help you navigate the vast field of data science, making it a great starting point for beginners.
It includes an interactive website that will teach you the basics of statistics and Python. It will help you learn the different steps involved in a proper data science project. You will learn about machine learning models, data processing and visualization techniques, automation, and more.
2. Python Data Science Handbook
Repository: jakevdp/Python Data Science Handbook
This repository contains the full text of the “Python Data Science Handbook” in Jupyter Notebooks. You can read the book for free and even run the notebook in Google Colab to experience various data science tasks in real-time. It covers essential data science libraries in Python, such as NumPy, pandas, Matplotlib, Scikit-Learn, and more. It’s a great starting point.
3. Data Science for Beginners
Repository: microsoft/data-science-for-beginners
This Microsoft repository offers a 10-week, 20-lesson curriculum designed for beginners. It provides comprehensive lessons and hands-on projects to build a solid foundation in data science concepts and techniques.
Each lesson includes an outline note, a supplementary video, a pre-lesson warm-up quiz, a written lesson, guides, knowledge checks, challenges, supplementary reading, assignments, and post-lesson quizzes.
4. IPython Notebooks on Data Science
Repository: Donnemartin/ipython-data-science-notebooks
This repository includes a collection of Jupyter notebooks covering various data science topics including deep learning, machine learning, data analytics, and Python basics. It is a valuable resource for hands-on learning. The content is divided based on tools such as scikit-learn, scipy, pandas, matplotlib, numpy, python-data, spark, and more.
5. Applied machine learning
Repository: eugeneyan/ml applied
The repository focuses on applied machine learning and offers technology articles and blogs from companies sharing their real-world work on data science and machine learning. It is an excellent resource for learning how to implement machine learning in production environments.
The list is divided based on topics such as data quality, data engineering, feature storage, classification, regression, forecasting, recommendation, search, and ranking, among others. It mainly focuses on machine learning and how to implement machine learning projects.
6. Path to free, self-taught training in data science
Repository: bone/data-science
This repository offers a complete curriculum for self-paced data science education. It includes links to free courses, textbooks, and resources covering everything from basic math to advanced machine learning.
You should read my blog, Enroll in a Data Science Undergraduate Program for Free, which covers various aspects of the program and explains how you can enroll and start learning.
7. The Masters of Open Source Data Science
Repository: Masters in Data Science/Go
This repository offers a comprehensive open-source curriculum designed to prepare students for entry-level positions in the field of data science. The goal is to provide free, high-quality educational resources that rival the caliber of materials found in the most prestigious paid programs. By leveraging open-source materials, this curriculum ensures that beginners have access to the best learning resources without financial barriers.
8. Amazing Data Science
Repository: academic/amazing-data science
This repository is a curated list of great data science resources, including tutorials, books, software, and tools. It is a must-go reference for everyone to learn and apply data science to real-world problems. In addition to the list of resources, it also explains how to start a career in data science. I highly recommend you bookmark it and use it whenever you want to discover new tools or learn new concepts. It is maintained by the open source community, which means you will get the latest and most up-to-date information.
9. Data Science Interview Questions and Answers
Repository: alexeygrigorev/interviews-on-data-science
Are you preparing for a job interview in the field of data science? This repository offers a collection of questions and answers on this topic. It is an excellent resource to understand the types of questions you might face and prepare your answers.
The repository is divided into two parts: theoretical and technical questions. In general, it covers questions on SQL, Python, classification, regularization, feature selection, decision trees, and more.
10. Data science with a conventional pattern
Repository: DrivenData.org/cookiecutter-data-science
This repository provides a standardized project structure for data science projects. It helps ensure that your projects are organized, reproducible, and shareable, following best practices for data science work.
Having a well-structured data science project template can significantly alleviate many challenges related to collaboration and reproducibility. Not only does it streamline teamwork by providing a consistent framework, but it also improves your ability to fix errors and resolve issues more efficiently.
Final Thoughts
Whether you are a beginner looking to build a solid foundation or a seasoned professional looking to expand your knowledge, these 10 repositories provide valuable content to improve your data science skills and expertise. They consist of tutorials, interactive books, courses, project code examples, free resources, research papers, project templates, college syllabi, and more. Simply bookmark them and use them while learning new tools or concepts.
Abid Ali Awan (@1abidaliawan) is a certified data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in technology Management and a Bachelor's degree in Telecommunication Engineering. His vision is to create an ai product using a graph neural network for students struggling with mental illness.
