In the Florida Everglades, a non-indigenous species has taken over – pythons. These snakes have wiped out entire ecosystems in southern Florida, to the point authorities there held a “Python Challenge” where willing citizens were encouraged to kill as many of the constrictors as they could.
The world of software engineering is experiencing a similar phenomenon (sans the negative consequences). The programming language Python has also taken over, as Stack Overflow has found it to be the fastest-growing programming language in the world.
Why is Python – which was first released in 1991 – suddenly so popular? Two words: data science.
“Python’s popularity in data science and machine learning is probably the main driver of its fast growth,” Stack Overflow Data Scientist David Robinson said in a blog post, who used data science techniques to come to the conclusion.
Interestingly enough, Stack Overflow named R – another programming language typically associated with data science – the second-fastest growing language in the world. Additionally, we here at LinkedIn Learning have seen a steep increase in the popularity of Python, R and other data science-related courses over the past year.
Clearly, much like the snakes in Florida, data science and its related programming languages have dominated parts of the tech landscape, forever changing the talent ecosystem.
Why is Python – and data science in general – so popular?
There is evidence beyond Stack Overflow’s findings that data science has become the hottest trend in business today. Recently, we at LinkedIn analyzed the skills the top companies in America are looking for. Eighteen of the top 20 – 90 percent! – of the skills top companies are looking for the most directly relate to data science.
Why? Organizations today collect so much data, they hardly know what to do with it all. They are in desperate need of strategic data scientists who know how to sift through it all to uncover the patterns that’ll drive business results.
Python in particular is exploding for a variety of reasons. In a blog post, LinkedIn Learning Data Science Content Manager Steve Weiss said the most common languages among data scientists are R, Python, SQL and Java.
SQL is more for managing databases. R, Python and Java are more for analytics, modeling and visualization, Weiss wrote.
Among those three, R is the most complex, Java the simplest. Python is in the middle; neither too rudimentary like Java, nor too inaccessible like R.
“Python is arguably the in-between: it can do a lot, it’s fast and it’s scalable,” Weiss wrote. “In any skills market that values a good-enough-for-enough-uses solution, the solution is usually Python.”
Python has the additional benefit of having pure software development functionality as well, as it’s one of the world’s most versatile programming languages. For that reason, it also fits nicely into most DevOps processes.
Again though, learning any language related to data science is a smart career move. Study after study shows it’s something organizations are hungry for, and it’s lucrative as well: LinkedIn salary data shows the average data scientist in the United States earns $113,000 annually.
Looking to learn Python, the fastest growing programming language in the world? These LinkedIn Learning courses can help:
- Python for Data Science Essential Training
- Learning Python
- Python: Programming Efficiently
- Building a Recommendation System with Python Machine Learning & AI
- Python for Data Science Tips, Tricks, & Techniques
Topics: Productivity tips
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