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Python vs R in Data Science: Know which is in Trend

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Data Science has become the most desirable jobs these days, but there is a huge issue for the job seekers in terms of deciding what course they should opt that would benefit them the most in the getting the job with a good salary package. The most required technologies of the Data science field are Python and R. Let’s see which one is superior.

R has the bigger library than Python of statistical packages

R is the much better option when it comes to the statistical works. Focusing on the expansion of the package, R covers a wide range of the stastical tasks; by using the CRAN tasks view you will get to know that it contains all from Psychometrics to Genetics to the Finance. On the other hand, Python facilitates packages like statsmodels; both Python and R shares some of the common techniques, but, R is way ahead than Python.

Python is considered better for building the analytics tools

Even though, both R and Python shares the most of the common techniques, their usage satisfies different requirements. If you want to develop a tool or a service that supports the use of data analysis then Python is the best choice. With the use of Python you can create the websites that can interact with the various databases and manage the users simultaneously.

Python supports deep learning

The packages in Python: Lasagne, Keras, tensorflow and caffe make creating deep neural networks straightforward, whereas R is more for data visualization. Though, the package like tensorflow being ported to R, but, Python is very handy when it comes to deep learning.

R supports data visualization

R consists package like ggplot2 which makes it more handy and customizable when it comes to perform data visualization tasks. Unlike R, Python has made its mark in the department of interactive plots, but R is the best suitable option for data visualization.

Python relies on packages, whereas R indulges in creating the data analysis functionalities

Python is known as the most common programming language and having the package like Numpy and pandas this serves most of the data analysis functionality. However, R has been designed to support the statistical activities in data analysis.

Nevertheless, both the technologies serve different purposes; so first analyse your requirements and then choose the course as both of them are high in the demand list of the organizations.

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