Academic data scientists have taken up the role of AI engineers and it is now absolutely necessary to learn Git for data science. For performing collaborative coding in small as well as large data science organizational teams. For building a personal project portfolio on an online repository hosting platform such as GitHub, GitLab, etc. For tracking code as well as file changes. For contributing and learning from open-source data science projects.
Ever since the invention of Git, it has widely been used by software developers for tracking code as well as files changes.
However, in the past few years, the adoption rate of Git for data science has seen a tremendous increase as well. You might even find that the knowledge of Git is now a requirement for various data science vacancies posted on a daily basis on the internet.
Git is one of the most commonly used command-line tools used for version control in software development. Through version control, developers are able to record, track and save changes to files over time so that they can quickly revert back to previous versions of the files whenever needed.
Also, popular platforms such as Github and Gitlab use Git as the underlying backbone to empower developers to seamlessly work together on the same project at the same time. Many open-source projects (including Linux) were made possible due to Git where thousands of people came together to work on a single project despite the difference in geographical locations.
Today, academic data scientists have taken up the role of AI engineers in building the startups of the future and thus, this brings Git into the limelight as a near-to-perfect tool for version control.
Here are a few major reasons why a modern day data scientist should learn Git:
Fortunately, learning Git will be a piece of cake if you are able to remember the hundreds of algorithms you study in data science.
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