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Summary

The website content provides a curated list of 10 essential GitHub repositories that offer valuable resources for both novice and experienced data scientists to enhance their skills through hands-on learning, tutorials, and project-based examples.

Abstract

The article on the website emphasizes the critical role of data science in modern business decision-making and highlights the importance of practical experience in the field. It introduces ten GitHub repositories that serve as a treasure trove for data scientists, offering resources ranging from comprehensive roadmaps and free programming books to collections of machine learning algorithms and project-based tutorials. These repositories cover various aspects of data science, including machine learning, neural networks, data engineering, and data visualization, and utilize popular libraries such as TensorFlow, sci-kit-learn, pandas, and matplotlib. The article encourages readers to explore these repositories to learn, grow, and stay updated with the latest developments in the field. It also provides direct links to the repositories and invites readers to subscribe to a Medium newsletter for more content and to support the writers.

Opinions

  • The author believes that gaining hands-on experience is crucial for learning data science.
  • GitHub is presented as an excellent platform for data scientists to access a wealth of learning resources.
  • The repositories listed are considered valuable for data scientists of all levels, from beginners to advanced practitioners.
  • The author expresses a personal preference for the Open API repository, highlighting its utility for finding reliable data sources.
  • The article suggests that staying informed about the latest tools and libraries is important for data scientists, as evidenced by the inclusion of repositories like "Awesome Data Science."
  • The author encourages engagement and contribution from the community by inviting readers to comment with additional useful GitHub repositories.
  • The author promotes their Medium newsletter, indicating a belief in the value of their content and its potential to benefit readers interested in data science.

10 useful Github repositories every Data Scientist should bookmark

And where to find them

Self-made image.

Data science has become a vital part of modern business decision-making, with organizations of all sizes relying on it to drive growth and success. With the increasing popularity of the field, more and more individuals are interested in learning about data science.

Gaining hands-on experience is crucial for learning any field, and data science is no exception.

One of the best places to start is GitHub, which offers a wealth of resources for learning and growing in the field.

This is why, I collected 10 different Github repositories that can be useful for any Data Scientist — either beginners or seniors — who wants to keep learning and improving their skills.

Let’s discover them all together 👇🏻

#1. Data Science Roadmap

If you’re looking to become a data scientist, this repository is here to help. It’s based on a roadmap created by Swami Chandrasekaran, and it covers everything you need to know to crush into Data Science and become a succesful data scientist.

Screenshot of the Dat Science Roadmap GitHub Repository.

From the fundamentals to statistics and programming, and then on to machine learning, data visualization, and data munging — this GitHub will allow you to crush into Data Science and learn fast!

⚠️ Additionally, you’ll find a section dedicated to tools that data scientists commonly use in their work.

#2. Free-programming-books

Books are still an important source of knowledge for any field — and Data Science is no exception. This GitHub repository contains a huge list of freely available books to learn anything related to programming — be it Python, Machine Learning or any other!

Screenshot of the Free-programming-books repository.

#3. Awesome repository

The Awesome Github repository provides an organized list of machine learning libraries, frameworks and tools in almost all the languages available.

Screenshot of the Awesome repository.

With libraries like sci-kit-image, CLTK, sci-kit-learn, pandas, and neural_talk, it’s a one-stop-shop for all your Data Science needs!

#4. A curated list of project-based tutorials.

This GitHub repository provides a collection of tutorials for creating projects of any kind. While it covers a diverse range of subjects useful for any developer out there, it also includes many projects that are especially useful for data scientists.

Screenshot of the Project Based Learning repository.

Learn to build your own app, covering various primary languages, these tutorials guide you from scratch!

#5. Open API

This Github comes in really handy when looking for some reliable data source of any kind. It contains a collective list of free APIs for use in software and web development. One of my favorites for sure!

Screenshot of the Open API repository.

#6. The Algorithms

This repository offers a collection of Python algorithms for various domains such as Machine learning, Neural Networks, Digital Image Processing, and Computer Vision.

It includes codes for regression, classification, backpropagation, Convolutional Neural Networks, edge detection, and pooling, useful for various applications like predictive analysis, image classification, and autonomous cars.

Screenshot of the Algorithms repository.

#7. Data Science Python Notebooks

This repository offers python notebooks on machine learning, data engineering, and data augmentation using popular libraries such as TensorFlow, sci-kit-learn, pandas, and matplotlib.

Screenshot of the Data Science Python Notebooks repository.

It includes examples of popular machine learning algorithms, data cleaning, and visualization techniques.

#8. Home-made machine learning

This repo offers a comprehensive collection of machine learning algorithms, explained with code and mathematics, using Python and Jupyter notebooks.

Screenshot of theHome made ML repository.

It covers supervised and unsupervised learning, as well as neural networks, making it a valuable resource for understanding and strengthening the fundamentals of machine learning.

#9. Awesome Data Science

This GitHub repository is essential for those who want to learn the basics of Data Science and Machine Learning, including tutorials and free courses. It also includes popular libraries, journals, and podcasts for staying up-to-date on the latest developments.

Screenshot of the Awesome Data Science repository.

#10. 500 AI-ML Projects

This repository offers a comprehensive list of over 500 projects on machine learning, NLP, and AI, complete with code, to give you hands-on experience in the field.

Screenshot of the 500 AI-ML Projects repository.

It’s perfect for students or enthusiasts looking to gain practical knowledge and create projects for their resumes — and get some extra inspiration for your next personal project! :D

Hope you find these resources useful! :)

If you have any further question, or you know any other useful Github repository, feel free to comment!

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