Introduction to GitHub for Data Science

Nowadays Version Control has become an essential accessory for all data scientists. Version Control enables data scientists to work cooperatively in a team, making collaboration in projects easier, sharing works, and aiding other data scientists to repeat similar processes. Even if you are a data scientist who loves to work alone it is always easy to retreat to the prior change or changing or making a change to the branch first, before merging your new changes into the current process.

What is GitHub?

 GitHub is one of the most popular and repeatedly used platforms for Version Control. GitHub uses an application known as git to add version control to your code. Different files of a project are stored in central remote storage known as a repository. The repository is sort of like a depository, but here you won't be saving money. Instead, you are storing data of your project, which can be accessible by any person whom you've permitted it. Every time you make certain changes in your project locally will add up to the main directory once you put in the command to push. If you want to go back to a prior state when you hadn't made the change, at that moment this record helps you to do it.

 Because of the nature of the storage or repository, anyone with permission to access can go into it and make some changes to the project. You can make a copy of your current project and store it on your local device separately. so, you can work on it without the fear of ruining your project. especially when you are working on a project where production of the model is entirely reliant on the codings.

Why is it important for a data scientist?

 As mentioned earlier, data scientists use GitHub for collaboration with other data scientists, making changes to the project efficiently and being able to track and rollback changes over the time.

 In the past, there wasn't any need to use version control by the data scientist, as it was given out to the software or data engineering team to process it into production. However, as technology is evolving and growing rapidly it has become more accessible for the data scientist to write their codes into the project to put models in the production. That is why it is becoming more important for the data scientist to use version control themselves.