Is GitHub useful for data science?

Data scientists need to use Github for much the same reason that software engineers do — for collaboration, ‘safely’ making changes to projects and being able to track and rollback changes over time. It is, therefore, becoming more and more important that data scientists are proficient in the use of version control.

What is the most popular GitHub repo?

Repositories

Rank	Repository	Star
1	freeCodeCamp	337,037
2	996.ICU	260,100
3	free-programming-…	214,658
4	coding-interview-…	200,435

What is repo in data science?

The purpose of the “Data Science Knowledge Repo” is to provide a central resource that data scientists can revisit frequently to refresh knowledge or learn new skills.

How do I make a good repository on GitHub?

Top GitHub best practices guide for developers [expanded Dec 2019]

1. 0 – Don’t git push straight to master.
2. 1 – Don’t commit code as an unrecognized author.
3. 2 – Define code owners for faster code reviews.
4. 3 – Don’t leak secrets into source control.
5. 6 – Create a meaningful git ignore file.
6. 7 – Archive dead repositories.

How do I make a GitHub repository professional?

Here are a few tips to create a more attractive profile in GitHub…

1. Update the code constantly. Recruiters check, not only the content, but also the activity of your GitHub account.
2. Write clean, commented, and well-organized code.
3. Use Github Pages.
4. Work as a team.
5. Contribute.

How do I make my GitHub repository popular?

How to get up to 3500+ GitHub stars in one week

1. Write a beautiful README. On GitHub, the README file is like the landing page of your website.
2. Be clear about what you need.
3. Get people to the GitHub page.
4. Be where developers are.
5. Ask communities for feedback.
6. Email campaign.
7. Add a tweet button.
8. Little to no advertising.

How do I make a GitHub data science portfolio?

Let’s get started!

1. Step 1: Create a GitHub Account. First, we need to sign up a GitHub account at https://github.com/.
2. Step 2: Create a Repository Named user-name.github.io.
3. Step 3: Customize Our Portfolio.
4. Step 4: Upload Our Projects.

Who are some of the most influential data scientists on GitHub?

Here’s is a compiled list of most influential data scientists on Github to follow. These data scientists are experts in their respective field which ranges from python, machine learning, neural nets, data visualization, deep learning, data science etc. 1. Sebastian Raschka (Machine Learning, Data Visualization) 2.

Which GitHub repository should I use to learn?

GitHub repositories contain a wealth of valuable learning resources, but that doesn’t mean you need to use all of them. My suggestion is to first take one of the “beginner to intermediate” type repositories and work through all the learning material in it. You should work through the repository that best fits your learning style.

Can data science be used on GitHub?

Data Science in Github. Since its creation, GitHub has been known to be the dwelling place for software engineers. But as data grew exponentially and languages such as Python and JavaScript became popular, GitHub has become a place for the massive community of data science enthusiasts and professionals.

What is the data science repository?

As mentioned in the readme.md, this repo is a trove of carefully curated resources and links (on the topics of software, platforms, language, techniques, etc.) related to data science, all in one place.