What makes Python a popular choice in data science?

If you’ve been researching the subject of data science recently, it’s likely that you’ve come across more than a few mentions of Python during your efforts.

Python is a general-purpose programming language of significant popularity in this field due to its speed, high flexibility, and many functionalities.

As such, it is also an important element of Southampton Data Science Academy’s Fundamentals of Data Science (Technical), our 6-week course designed to teach individuals how to harness the power of data insights for their workplace.

However, if you’re still unsure about how the language relates to the demands or processes of a career in data science, here are some key reasons why Python plays such a key role.

All-rounder functionality

As a general-purpose programming language, Python can be used to write coding for a wide variety of programs. Thereby, it can be employed to help solve any number of challenges relating to data analytics and beyond.

This also allows for a great deal of creativity and flexibility. Programmers can script applications for new or niche tasks without being held back by limited capabilities.

Concise, comprehensible code

The language also places heavy emphasis on creating readable code. Its clean design philosophy enables users to write code that is clear and logical. This in turn helps professionals save time as they work, as they need to type fewer lines of code to accomplish a desired task.

Linear learning curve

With its emphasis on straightforwardness and simplicity, Python’s relatively smooth learning curve means that many consider it a strong choice for those just entering data science.

Its intuitiveness also typically means that programmers with experience in other languages can familiarise themselves with Python without much difficulty.

Numerous visualisation tools

Python also boasts a variety of graphic and visualisation options, which are a crucial aspect of working in data analytics.

The ability to simply communicate or demonstrate trends in data cannot be understated. Python’s diverse visualisation tools help those working in data science make their output more accessible to others through graphs, timelines, and so on.

A rich ecosystem

In this context, “ecosystem” is used to describe the software packages available to users of a specific language. These software interact and evolve together to serve a range of purposes – hence the name.

Given its large following, there are a vast array of software libraries for Python that can help streamline data analysis to better suit a user’s needs.

If you’re interested in studying data analytics with our Fundamentals of Data Science (Technical) but don’t feel you have enough experience with Python, our team can offer additional support.

We provide a pack of five intensive, one-hour tutorials that will equip you with the foundational knowledge needed to begin the course.

Upon completion of these tutorials, you’ll have the capabilities and confidence to navigate any challenges you may encounter during your studies:

Learn more here >

Topics: fundamentals of data science

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