10 GitHub Repositories to Master Data Science

Learning data science through courses or YouTube videos can become monotonous as it often involves passive consumption of information. You are not getting your hands dirty, experimenting, or actually building anything. You are simply absorbing content from a screen. But what if I told you that there is a more engaging and effective way to grasp data science tools and concepts? That’s right. Today, we are going to explore 10 GitHub repositories that will help you master data science concepts through interactive courses, books,  guides, code examples, projects, free courses based on top university curricula, interview questions, and best practices.

1. Virgilio: Your Data Science Mentor

Repository: virgili0/Virgilio

Virgilio is a comprehensive guide and mentor for data science e-learning. It provides structured content, tutorials, and resources to help you navigate through the vast field of data science, making it an excellent starting point for beginners.

It comes with an interactive website that will teach you the fundamentals of statistics and Python. It will help you learn various steps involved in a proper data science project. You will be learning about machine learning models, data processing and visualization techniques, automation, and more.

2. Python Data Science Handbook

Repository: jakevdp/PythonDataScienceHandbook

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 is a great starting point. 

3. Data Science for Beginners

Repository: microsoft/Data-Science-For-Beginners

This repository from Microsoft 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 a sketch note, supplemental video, pre-lesson warm-up quiz, written lesson, guides, knowledge checks, challenges, supplemental reading, assignments, and post-lesson quizzes.

4. Data Science IPython Notebooks

Repository: donnemartin/data-science-ipython-notebooks

This repository includes a collection of Jupyter notebooks covering various data science topics, including deep learning, machine learning, data analysis, and Python essentials. It is a valuable resource for practical, 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/applied-ml

The repository focuses on applied machine learning, offering papers and tech blogs by companies sharing their real-world data science and machine learning work. It is an excellent resource for learning how to implement ML in production environments. 

The list is divided based on topics such as data quality, data engineering, feature stores, classification, regression, forecasting, recommendation, search & ranking, and more. It heavily focuses on machine learning and how to implement machine learning projects.

6. Path to a Free Self-Taught Education in Data Science

Repository: ossu/data-science

This repository provides a comprehensive curriculum for a self-taught education in data science. It includes links to free courses, textbooks, and resources, covering everything from foundational mathematics 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 Open Source Data Science Masters

Repository: datasciencemasters/go

This repository offers a comprehensive, open-source curriculum designed to prepare students for entry-level data scientist roles. The aim is to provide high-quality, no-cost educational resources that rival the caliber of materials found in the most reputable paid programs. By leveraging open-source materials, this curriculum ensures that the beginners have access to the best learning resources without financial barriers.

8. Awesome Data Science

Repository: academic/awesome-datascience

This repository is a curated list of excellent data science resources, including tutorials, books, software, and tools. It is a go-to reference for anyone looking to learn and apply data science to real-world problems. Apart from the list of resources, it also explains how to get started with a data science career. I recommend you bookmark it and use it when you want to discover new tools or learn new concepts. It is maintained by the open-source community, meaning you will get the latest, most up-to-date information. 

9. Data Science Interview Questions and Answers

Repository: alexeygrigorev/data-science-interviews

Preparing for a data science job interview? This repository offers a collection of data science interview questions and answers. It is an excellent resource for understanding the types of questions you might face and preparing your responses.

The repository is categorized into two parts: Theoretical and Technical questions. Overall, it covers questions on SQL, Python, classification, regularization, feature selection, decision trees, and more.

10. Cookiecutter Data Science

Repository: drivendataorg/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 enhances your ability to fix bugs and resolve issues more efficiently.

Final Thoughts

Whether you are a beginner looking to build a strong foundation or an experienced practitioner seeking to expand your knowledge, these 10 repositories provide valuable content to enhance your skills and expertise in data science. They consist of tutorials, interactive books, courses, project code examples, free resources, research papers, project templates, university curriculums, and more. Just bookmark them and use them while learning new tools or concepts.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing 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 build an AI product using a graph neural network for students struggling with mental illness.