In recent years, Python and R have come to the fore in data analysis. Python, a general-purpose language, has become very popular. Python is used even by large companies. However, another software language used in statistics studies, especially in academic studies and and also in the software industry, is R. Now, when you decide to learn a new software language in the field of data science, should it be Python or R? The main answer to this question will be your “needs”. Let's briefly list the strengths and weaks of both languages so that you can determine this.
Let's start with the coding language first; Python is more user-friendly and easy to learn, however it’s not difficult writing code in R. If your code block grows, it's easier to maintain in Python than in R.
We can't write every code block, libraries for that. There are libraries for data collection, classification, analysis, manipulation and drawing for both languages. R is leading in data analysis and statistical applications. R has extensive libraries useful for these tasks. It also does data visualization well. Such these libraries continue to be developed in Python.
Both languages are open source. Python runs faster than R; you need to install many packages in R. It is seen that Python is more capable than R in web applications.
As a final word, if your studies are in the field of statistics, R is easier and more reliable with its rich libraries. If you are going to work on machine learning, Python can be preferred with the maintenance of the codes and many of the features we have mentioned. It is useful to try both at first, then you can deepen in the coding language you want.
Asst. Prof. Orhan Özaydın, CMA
