topic: Why Python and Git

Data science is about statistics, insights, knowledge. Data science is made possible by a set of tools.

Python and Git are 2 such tools.

You’ll start learning about them shortly, but before you do, it would be useful for you to know a bit about why these tools matter.

Why Python

Python is a programming language that is designed to be both powerful and user-friendly. It offers a gentle learning curve for beginners while packing the punch needed for complex data analysis.

Python’s simple style means that you spend less time grappling with the language’s syntax and more time analyzing and interpreting data.

It’s has become the standard language of data science as it is used in many tools that are fantastic for wrangling, displaying and modelling data.

What else can Python do?

If you learn Python programming then you are not limited to only doing data science. Python is a versatile language with applications in web development, automation, business tool development and even space travel.

Why Git

Programming has been around for a while and best practices have been established over the years.

Git is an essential tool when it comes to version control and collaboration.

Git allows individuals and teams to track changes in their code, revert to previous versions when necessary, and manage different development branches concurrently. This is crucial in a field like data science, where experiments and analyses must be reproducible and where projects often evolve over time.

Git also facilitates collaboration among data scientists and other stakeholders by enabling a shared history and easy integration of changes in a project. By mastering Git, a data scientist can enhance project organization, streamline collaboration, and ensure the integrity and continuity of their work, making it an indispensable skill in the modern data science toolkit.

Learning Git & Python

Learning Git and Python might initially seem daunting or unfamiliar. This feeling is entirely normal and expected.

We include these tools in our curriculum precisely because they are vital for the success of a professional data scientist. Our focus is on nurturing professional competencies, ensuring that you are well-equipped for the challenges and opportunities in the field of data science.


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