A unique aspects of data science training is the balance between learning both theoretical and practical skills. Students are expected to understand theories that take a deep dive into statistics, mathematics, and even philosophical ideas; but they must also learn how to work on projects or as part of a data science team. A good data science curriculum includes training in popular programming languages and analytics technologies as well as software required to complete projects and engage in collaboration using real-world data and methods.
Teaching novice data scientists to collaborate on team-based projects means learning more about practical tools that make the process of completing those projects easier and more efficient. One example of a practical tool that every data scientist should know is how to use Git to save and share files. Not to be confused with GitHub, Git is an open-source version control system widely used to track the changes that are made to a data science project or product. Whether you are a data scientist or software developer, Git is an essential tool for workflow management, team-based research, and collaboration.
What is Git?
Git is open-source software that can be used to track file changes and workflows and is commonly used among programmers and developers to develop a product or collaborate on a data science project. Originally created as part of the Linux operating system, Git is widely used in software and data carpentry coursework because of its incorporation of version control. Version control is a file management tool often used to save a version of a file that tracks the changes that have been made to that file over time.
This system allows any user to easily go back and see what changes have been made by an individual or multiple data scientists working on the same document at the same time. Through merging and parallel processing, these discrete changes can be saved in a repository which can be accessed at any time. Git repositories are then stored in the “.git Directory,” which keeps track of the file history and workflow models.
Git is an important part of any process that involves collaborative development and design, because it allows team members to clearly see changes made at every stage. Individual data scientists also benefit from using version control with Git to efficiently save their documentation.
The Difference Between Git and GitHub
Despite the similarities in name, Git and GitHub are two different tools, though commonly used in conjunction with each other to save and share files. Git is a version control tool used to track and save changes within a terminal or notebook interface, while GitHub is used to share and collaborate in the repositories containing the files saved in Git. Github is a cloud-based development platform used by a range of companies and teams that require access to raw data, packages, and libraries that are useful for their own research and development.
Building on the open-data movement, platforms like GitHub were created to support the ease of sharing information and collaborating on projects. Fostering transparency and accountability in the field, the Github platform advances open-source movements that encourage scientists and researchers to work in teams to solve the world's problems. To support the open data and open-source movements, many data scientists use GitHub to upload their own project files as well as to access public datasets and the projects of other researchers. GitHub is one of many data science tools which help build online communities through resource sharing among data scientists and developers working on similar projects and goals.
How Data Scientists and Developers Use Git
File systems created by cloud providers, such as Google, save work and track document changes in real-time, ensuring that information and data are never lost. Similarly, using Git allows data scientists and developers to maintain version control as well as continued access to archived versions of a product or project, ensuring that files are never lost to a system failure or glitch. Additionally, features like parallel development and Worktrees enable data scientists and developers to create a system that can manage large collections of data and workflow.
These features are also Git’s main appeal to scientists and developers working on a data science team or collaborative project. Saving different versions of the project over time allows team members to keep track of changes made, and also who made those changes. For product developers, this makes it easier to return to earlier models or prototypes. And for data scientists, Git simplifies the process of reusing or editing previous programs and lines of code. Combining Git with GitHub also means that projects are easily reproducible when shared with other data scientists working on similar projects. Reproducibility is essential to open science as well as to contracted data science teams or consultants handing a project over to a new team.
Want to use Git for Your Next Data Science Project?
Anyone who has worked with computers or technology understands the importance of backing up files and changes over time, which makes tools like Git an essential part of the data science curriculum. The curriculum for Noble Desktop’s Data Science Classes and Certificate Programs includes hands-on experience and training with both Git and GitHub. The Data Science Certificate course introduces students to programming with Python and SQL through creating projects that use both Git and GitHub. The Python Programming Bootcamp also focuses on using object-oriented programming to create data science projects and a portfolio with Git. In addition, the Python Developer Certificate trains future developers and data science professionals in Git and SQL. So, any data science students that want to learn more about the practical methods of managing a data science project or creating a portfolio should learn how to use Git!