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Awesome lists are some of the most popular repositories on GitHub, often attracting thousands of stars from the community. These curated lists gather high-quality resources, tools, and tutorials on a specific topic, making them valuable references for developers and learners alike.
However, simply adding the word “awesome” to your repository name does not guarantee that you will receive a lot of stars automatically. The popularity of an awesome list depends on the quality and usefulness of its content, as well as its visibility within the community. If your awesome list is officially verified or included by the original Awesome List creator, sindresorhus, it can significantly boost your repository’s visibility and credibility. People trust the “awesome” brand.
In this article, we will review some of the most popular and impressive lists for data science. We will explore collections of tools, resources, tutorials, guides, and learning paths, all designed to help you maximize your learning journey in data science.
1. Awesome Python: The Ultimate Python Resource List
Link: vinta/awesome-python
Here is a comprehensive list of Python frameworks, libraries, software, and resources that have been around for at least 10 years and are still actively maintained. This is a must-have bookmark for any data scientist working with Python, encompassing everything from data analysis and machine learning to web development and automation.
2. Awesome R: Essential R Packages and Tools
Link: qinwf/awesome-R
Finding the best R tools can be challenging, as its community is relatively small compared to Python. This collection of the top R packages, frameworks, and software provides a one-stop shop for discovering all kinds of R packages for various use cases. Whether you are interested in data manipulation, visualization, or statistical modeling, this list is your gateway to the R ecosystem.
3. Awesome Public Datasets: High-Quality Open Data
Link: awesomedata/awesome-public-datasets
Here is a curated list of high-quality open datasets, organized by topic. It is ideal for data science projects, machine learning experiments, and anyone who wants to work with real-world data. After Kaggle, this is one of the best sources for free datasets to download and enhance your data science portfolio.
4. Awesome SQLAlchemy: Tools for Python’s Leading ORM
Link: dahlia/awesome-sqlalchemy
It is a list of tools, extensions, and resources for SQLAlchemy, Python’s most popular ORM. Ideal for data scientists and engineers working with databases and complex data models.
5. Awesome Data Science: Learn and Apply Data Science
Link: academic/awesome-datascience
An open-source repository that helps you learn data science from the beginning and also assists you in building a strong portfolio by working on real-life problems. It includes tutorials, courses, books, and project ideas for all levels.
6. Awesome Learn Data Science: Curated Learning Paths
Link: siboehm/awesome-learn-datascience
A handpicked list of resources to help you get started with data science. Find beginner-friendly tutorials, MOOCs, books, and guides to kickstart your data science journey.
7. Awesome Analytics: Top Analytics Tools and Frameworks
Link: oxnr/awesome-analytics
A curated list of analytics frameworks, software, and tools. Great for all levels, including non-technical people who want to explore no-code tools for data science or social media analytics.
8. Awesome Machine Learning: The Best ML Libraries
Link: josephmisiti/awesome-machine-learning
A comprehensive and organized list of machine learning frameworks, libraries, and software across multiple languages. It also includes free machine learning books, courses, blogs, newsletters, and links to local meetups and communities.
9. Awesome Machine Learning Tutorials: Practical Guides and Articles
Link: ujjwalkarn/Machine-Learning-Tutorials
A collection of machine learning and deep learning tutorials, articles, and resources. Perfect for hands-on learners who want to deepen their understanding through practical examples.
10. Awesome Python Data Science: Curated Python Data Science Tools
Link: krzjoa/awesome-python-data-science
A carefully curated list of top Python packages for data science, encompassing various areas such as machine learning, deep learning, visualization, deployment, and more.
Conclusion
In today’s world of endless information, awesome lists are true gold mines for anyone serious about learning and building real skills. People are starting to realize that vibe coding is fun, but if you want to build a sustainable product, you need to learn the basics. That’s where these curated GitHub awesome repositories come in: they help you learn the basics, deepen your expertise, and stay up-to-date with the best tools and resources in the field of Data Science.
So, bookmark this page and explore the links that match your interests, whether you are learning a new language or diving into a specific topic.
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.
