Why Git Is Not Enough for Data Science

Why Git Is Not Enough for Data Science

Why Git is Not Enough for Data Science. TL;DR Git is used in almost every software development project to track code and file changes. Tremendous increase in Gits adoption for Data science projects. Git jump to the section “Why Git is not important to learn for data science”

Should a Data scientist learn Git?

TL;DR Git is used in almost every software development project to track code and file changes. Based on this ability to track every change, there has also been a tremendous increase in Gits adoption for Data science projects. In this post we discuss;

  1. Benefits of Git for data science
  2. The gaps and limitations of Git
  3. Best practices for using Git for data science projects

For those of you familiar with Git jump to the section “Why Git is important to learn for data science”

What is Git and how does it work?

“Git is a free and open-source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.” —  Git

As the description states, Git is a version control system. It helps record, track, and save any change made to source code and to quickly and easily recover any previous state.

Git uses a distributed version control model. This means that there can be many copies (or forks/remotes in the GitHub world) of the repository. When working locally, Git is the program that you will use to keep track of changes to your repository.

GitHub.com is a location on the internet that acts as a remote location for your repository. GitHub provides a backup of your work that can be retrieved if your local copy is lost (e.g., if your computer falls off a pier). GitHub also allows you to share your work and collaborate with others on a project.

Similar tools to GitHub are  GitLab,  Bitbucket.

Source:  Pro Git by Scott Chacon and Ben Straub.

open-source data-science git

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