The world of data analytics is ever-evolving. Job opportunities for analysts and scientists are skyrocketing. The need to learn cutting-edge programming languages is like never before. In order to handle humongous datasets, Python programming language is the best bet. When it comes to data handling and visualizations, it stays unrivalled. Though R programming language was once used, today Python is leading the show all alone.
Python programming language was launched back in the 1980s but it was only in 1989 it was turned into an application by Guido van Rossum at Centrum Wiskunde and Informatica (CWI) in Netherlands. From then, it has now become one of the most popular multi-purpose programming languages widely used by technicians for myriad development projects and purposes.
In case, you want an algorithm to be implemented into your product – Python has the ability to fulfill such tasks with ease. It is also equipped with face detection and text-to-speech conversion abilities. This means, if you are someone who is focused on doing things real-time, you have to seek Python.
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Moreover, many developers are leveraging Python’s extended TensorFlow library – it is being used to build advanced machine learning models, comprising of neural networks. Widely known, deep learning dimension of ML has been a game changer in the encompassing AI domain, and Python is the most dynamic language used in this arena.
But before Python, R programming language dominated the sphere of data analysis and visualization and still has its presence, far and wide. Developed by Robert Gentleman and Ross Ihaka at the University Of Auckland, R was intended to focus on statistical methodologies and strategies. Frankly speaking, visualizations are better in R – the reason being the easy availability of numerous libraries. They help plot the graphs. Ggplot2, patchwork and ggiraph are a few visualization packages in R. Being open-source, R boasts of many relevant educational sources up on the internet, where anyone can learn for almost no cost.
If compared loosely, R is definitely showing a downward trend in terms of search engine requests, making Python the most popular programming language but from a wider perspective, 71% of data scientists still use R at work, while 87% reported they prefer Python. The amount of difference is modest. However, the decline in the popularity of R is evident. In a recent study based on data engineer job descriptions, we found out that the demand for engineers proficient in R was substantially less as compared to Python. Almost 66% of data engineer job descriptions highlighted Python, whereas only 18% of descriptions mentioned R.
“Python is known to be an intuitive language that’s used across multiple domains in computer science,” the report stated. “It’s easy to work with, and the data science community has put the work in to create the plumbing it needs to solve complex computational problems. It could also be that more companies are moving data projects and products into production. R is not a general purpose programming language like Python.”
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The blog has been sourced from — www.techrepublic.com/article/why-python-is-the-real-language-of-data-science-not-r