Is Python or R the most suitable for data science? It is a key question for data science professionals, especially those who are starting.

For those who venture 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 about the processes and techniques of data science are what really matters to obtain a solid foundation in the world of data science.

So which language to choose?

For years, R was the obvious choice for those who started 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 ​​such as C, C ++, Java. Launched in 1997, R is common in a wide range of sectors and can be found from Wall Street to Silicon Valley as a good alternative to Matlab and SAS.

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 Data science training institute in bangalore present some reasons why Python can be chosen for data science.

Python is easy to use:

Python has a reputation for being easy to learn. With a readable syntax, Python is ideal for beginners or for data scientists who wish to acquire knowledge in this language.

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Python is versatile:

As a general purpose language, Python is a fast and powerful tool that has a lot of capacity. No matter what the problem you want to solve, Python can help you complete the task, thanks to the large number of libraries it has.

Python is better for building analysis tools:

R and Python are pretty good if you want to analyze a data set, 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 display tools, such as, ggplot2, rCharts and googleVis. Even though Python does not lend itself naturally to visualization, it has 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. PyPi is a useful place to explore the full scope of what the community is developing.

Python is better for Deep Learning:

There are many packages, such as 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 use Python for data science? Data science training in Bangalore considers Python to be a powerful and versatile tool that allows you to do more in less time. R, meanwhile, is a specialized tool, specifically designed for data analysis. In a market where diversification is becoming more and more a key in development, adding Python to your resume will allow you to obtain greater benefits.