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If you’re looking to start a career in Data Science, you’re probably wondering which learning route to go down. You’ve probably seen data science bootcamps pop up, courses on Udemy, degrees, and more. It can be hard to choose one route, when there are so many. 

What’s a better place to learn than GitHub repositories? For those of you who don’t know,  GitHub is a code-hosting platform for version control and collaboration. Who uses GitHub? You will see individual professionals, companies, university and bootcamp students, teachers, and beyond using the platform for collaborating and tracking code. 

Although GitHub is not the only platform of its kind, it’s very popular for these reasons: it is easy to use, supports both public and private repositories, and it's free on small scale projects. GitHub also has a community which helps to support all users on GitHub with questions, problems and their overall educational journey. Over the years, people have come to view GitHub differently, with some seeing it primarily for collaboration, others see it as a learning portal or go there to get inspiration.

So now that we know a bit about GitHub, let’s see how you can learn data science with GitHub repositories.

freeCodeCamp

Repository link: freeCodeCamp

If you’ve done a little bit of research on resources to learn data science, you probably have come across freeCodeCamp. Their resources are very popular and the biggest draw to them is that they are FREE. With 358k people stargazing over the repository, you definitely need to be part of the group.

You can also gain certifications in the following courses:

1. Responsive Web Design Certification
2. JavaScript Algorithms and Data Structures Certification
3. Front End Libraries Certification
4. Data Visualization Certification
5. APIs and Microservices Certification
6. Quality Assurance Certification
7. Scientific Computing with Python Certification
8. Data Analysis with Python Certification
9. Information Security Certification
10. Machine Learning with Python Certification

Data Science For Beginners

Repository link: Data Science For Beginners

One of the best GitHub repositories I have come across! This repo provided by Azure Cloud Advocates from Microsoft offers a 10-week, 20-lesson curriculum to help you break into data science. The lessons include a pre-lesson that is followed by a post-lesson quiz, written instructions on how to complete the lesson, with a solution, and an assignment.

This covers the basics of data science and is aimed at beginners. You will cover aspects such as data science ethics, introduction to statistics & probability, visualizing relationships, and more. 

The Open Source Data Science Masters

Repository link: The Open Source Data Science Masters

This GitHub repo provides you with a curriculum as well as resources. The majority of the resources are from universities and working data scientists which have a specific focus on the theory of data science as well as the applied skills.

A lot of the resources are free, the only cost is if you choose to purchase the recommended books. When you get to the end of the curriculum, you are encouraged to choose a project or dataset to demonstrate what you've learned. They have also provided a list of extracurricular study materials that can improve your knowledge base and skills. 

Free Data Science Books

Repository link: Free Data Science Books

If you’re a bookworm and the best way for you to learn is by flicking through pages - this GitHub repo has come to save the day. Not only does it provide a list of books that follow a curriculum, it's FREE! 

The books will state their difficulty level with beginner, intermediate, or veteran next to it. The topics covered include Data Science Introduction, Data Processing, Data Analysis, Data Science Application, Data Visualization, Uncategorized, and MOOCs about Data Science.

Data Science Curriculum

Repository link: Data Science Curriculum

When starting out on your roadmap to data science, it can be difficult to know where to start. This is the problem I had, and a lot of people I know. Following a curriculum will allow you to manage your time well, ensure you hit all the data science aspects and realize where your weaknesses are so you can work on them. 

This data science curriculum provided by Open Source Society University gives you a list of courses that you need in order to become a data scientist. They may not have the materials there for free, but having a study plan makes your life much easier. 

Awesome Data Science

Repository link: Awesome Data Science

Similar to a curriculum, this Awesome Data Science GitHub goes through all the nooks and cracks of data science. If you are the type of person that needs to know the topics you require to become a data scientist, but want to go and do your own research, this GitHub repository is for you. It’s the toolbox to data science

It provides you with books, blog articles, web pages and more on everything you need to know about data science. They also give you more information on free courses, intensive programs and colleges that can kickstart your data science career.

Data Science All Cheat Sheet

Repository link: Data Science All Cheat Sheet

Cheat sheets are a great way to learn something new. They provide you with the basic information and allow you to go off and study about it more. The owner pulled together these cheat sheets and aimed to help students achieve comprehensive content that provided clarity.

You have a wide range of areas that are provided with cheat sheets such as statistics, Matlab, machine learning, data warehouse, deep learning and more. 

Best of ML with Python

Repository link: Best of ML with Python

A key aspect of being a successful data scientist is ensuring that you can apply your skills and the only way to do this is by doing projects. Recruiters want to see your code, your train of thought and how you came to the output that you did. 

This Best of ML with Python GitHub repository provides you with 910 open-source projects grouped into 34 categories. The projects are ranked by a project-quality score, so you can see which projects are popular with a description of what the project entails. You have categories such as data loading & extraction, model interpretability, medical data, and more. 

Data Science Interview Resources - Interview Questions

Repository link: Data Science Interview Resources

Once you have mustered all the knowledge you require as a data scientist and have applied them into projects. The next step is to apply for jobs and prepare yourself for the interview. This is the most tricky part - but it's the moment you’ve been waiting for. 

The hard skill questions you will be asked during a data science interview will typically be based in two categories: theoretical and technical. This GitHub repo goes through both and helps you test your knowledge in order to prepare for your interview. They also give you tips on building your resume/CV, which is an important aspect when trying to win over the recruiter. 

Wrapping Up

Learning data science will not come easy, but with the amount of resources available in this day and age, it is definitely achievable. If you know of any other amazing GitHub repositories that can help others, drop them in the comments below.
 
 
Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.