Python vs R: Which is better for data science?
- Prwatech
- Mar 19, 2020
- 3 min read
It's a key question for data science professionals, especially those just starting out: Is Python or R best suited for data science?
For those venturing into the world of data science, it is important to master a language first, rather than trying to be an expert in each language. This is because knowing the processes and a technique of data science is what really matters to gain a solid foundation in the world of data science. Learn Python course and its features from our best python training Institute in Bangalore.
So which language to choose?
For years, R was the obvious choice for those starting out in data science, R was designed with statistics in mind, has a long history in the industry, has thousands of public packages, and integrates very well with languages like C, C ++, Java. Launched in 1997, R is common in a wide range of industries and can be found from Wall Street to Silicon Valley as a good alternative to Matlab and SAS. We Prwatech, India’s best R programming training institutes in Bangalore Offering advanced R Programming language course to those technology passionate who wanted to explore & brush up the technical skills from the scratch to advanced level.
On the other hand, Python offers many benefits, which means that an increasing number of people are adopting Python. It is true that Python is challenging the already established position of R as a programming language for data science. Here are some reasons why you can choose Python for data science.
Python is easy to use: Python has a reputation for being easy to learn. With readable syntax, Python is ideal for beginners or for data scientists who want to gain knowledge in this language.
Python is versatile: As a general-purpose language, Python is a fast and powerful tool that has a lot of capacity. No matter what problem you want to solve, Python can help you carry out the task, thanks to the large number of libraries it has.
Python is better at building parsing tools: R and Python are pretty good if you want to parse a dataset, but when it comes to building a web service for others to use the developed methods, Python is the way to go.
Data visualization with Python: This is where R generally beats Python. R has a wide range of visualization tools, such as, ggplot2, rCharts and googleVis. Furthermore, although Python does not lend itself naturally to visualization, it does have a wide range of libraries available, such as Matplotlib, Plot.ly and Seaborn.
The Python community is growing: Python has a large community, which includes a strong and growing presence in the data science community.
Python is better for Deep Learning: There are a lot of packages, like Theano, Keras, and Tensorflow, that make it really easy to create deep neural networks with Python, and although some of these packages are being ported to R, the support available in Python is very higher.
So should you be using python for data science? We consider Python to be a powerful and versatile tool that enables you to do more in less time. R, meanwhile, is a specialized tool, specifically designed for data analysis. In a market where diversification is increasingly becoming a key in development, adding Python to your repertoire will allow you to obtain greater benefits.
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