Many people have heard of Python and R, but for these two languages, many people are more entangled in choosing which one to choose. Next, let's take a look at the advantages of each.
Advantages of Python:
Most deep learning research is done in python, so tools such as Keras and PyTorch have python-first development. You can learn about these topics in Keras's Introduction to Deep Learning and PyTorch's Introduction to Deep Learning.
Another advantage of Python over R is the deployment of models to other parts of the software. Python is a general-purpose language. The process of writing applications in python and including Python-based models is seamless.
Python is called a universal language with an easy-to-understand syntax.
Advantages of R language:
A lot of statistical modeling research is done in R, so there are a wider range of model classes to choose from. If you have questions about modeling, R is the most appropriate.
Another trick of R is to use Shiny to easily create dashboards. Python also has Dash as an alternative, but it is not mature enough.
R's functions are developed for statisticians, so it has specific field advantages, such as the powerful features of data visualization.