Why Data Scientists Must Learn Python

Given the increasing role of machine learning and predictive analysis in today’s world, there is no question that data science is gaining in importance. That the star programming language for data science is Python is not disputed. But why is Python so popular, and what are some of the inherent strengths that make it a “must learn” language for data scientists?

The rise of data science

Before going into the strengths of Python, it is important to first understand why businesses are turning to data science. At the root of data science is a deluge of data as organizations digitalize globally. With access to a vast and growing pool of data, and with new KPIs such as customer experiences (CX), businesses need to find a way to sift through the data.

The objective is to gain extra competitive insights to help them pull ahead, measure performance, identify problem areas of their businesses, or to plan new marketing initiatives. And as the amount of data surges, the employees handling this task are first named business analysts, to data scientists today. Regardless of their designations, however, the remit ultimately revolves around the manipulation and analysis of data.

Inherent strengths

Python is officially the most widely used programming language in the world, beating the likes of Java and C. The reason behind this popularity, particularly with data scientists, is simple to understand: Python is easy to learn compared to the likes of R and MATLAB, it offers a myriad of inbuilt features that lend itself to developers, is highly extensible, and is excellent for implementing algorithms and managing data.

“Data scientists often need to incorporate statistical code into the production database or integrate the existing data with web-based applications. Apart from these they also need to implement algorithms daily. Python makes all these tasks a hassle-free affair for data scientists,” wrote business intelligence analyst James Warner in a Medium post.

According to Warner, Python is also much faster than languages such as R and Stata. And because data scientists can approach a problem using a variety of strategies, there is flexibility compared to more rigid languages that mandate only a certain way of doing things. (Of course, this might also make it more challenging for beginnings to learn – though Python as a language is so forgiving and robust that it barely matters)

The Python ecosystem

While its inherent strengths contributed to Python becoming a hit with developers around the world, it is the large number of code libraries that make the programming language a hit with data scientists. Data scientists can utilize data science libraries such as NumPy and SciPy to quickly start on scientific computing and technical computing tasks. And because so many data scientists already know Python, this also means that new data science libraries are likely to support it from the get-go.

And did we say that Python is an open-source language? This automatically lends itself to a community of open-source supporters and programmers who are often more than willing to offer advice or support for programming challenges. And even if they are not, there is a world of code samples in forums and Wikis across the Internet.

Finally, the popularity of Python lends itself to a virtuous cycle of success: As more data scientists use Python, more code and tools will be created solely for Python. And what it all means is this: an aspiring data scientist will need to brush up on Python to excel.

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