Python and R are both relatively popular programming languages, with a strong ecosystem and community, and are sought after and liked by everyone. Then in terms of data science, what is the difference between Python and R? Let's take a look.
Most deep learning research is done using python, so tools like Keras and PyTorch have python-first development, and you can learn about these topics in Keras's Introduction to Deep Learning.
Another area where python and R have advantages is to deploy models to other software. Python is a general programming language. Therefore, if you are writing applications in python, the process of including python-based models is seamless of. We introduced the deployment model and the construction of the python data engineering pipeline in the python design machine learning workflow.
Python is generally regarded as a universal language with an easy-to-understand syntax.
There are a variety of models to choose from when conducting large-scale statistical modeling research in R. Another big trick of R is to use Shiny to easily create dashboards. For those who don't have much technical experience, you can create and publish dashboards and share them.
R's functions were developed with statisticians' problems in mind to give advantages in specific fields, such as data visualization.
Python was originally a programming language used for software development. It can be easier to use for people who have computer science or software development, and it is easier to transition from other languages to python than R.