Python and R are the two top competitors, fighting to stand up as the best assistant for data scientists.
But if the practicality is taken into consideration, both of the robust software have different implementation horizons and are built to assist professionals from different fields.
Before setting up a mind for learning any of the languages, it is necessary to sort out the learning purposes and the languages’ implementation areas.
Here we present 5 steps to cover before choosing the right software that best suits your purpose, and will yield better futuristic outcomes.
Consider your Pre-Programming Knowledge
The syntax for Python finds a high resemblance with that of the simple English language. Therefore, learning Python is not a big deal even for the ones who do not have any pre-programming exposure.
On the other hand, R is comparatively difficult for beginners. The syntax for the language neither resembles any of the other programming languages nor does it find any resemblance with the verbal language.
Python’s easy syntax allows working with the codes more productively, but the R programming language’s unclear readability makes it a tougher language to work with.
Graphics Requirements
Python’s Matplotlib library is something productive that the language has embedded in itself. But considering the data set visualization tools provided by R, the more advanced and powerful options find an edge over Python.
Python visualization finds its excellent way in the basic cases, but when advanced statistics, datasets, and graphics are taken into account, R is preferred more than Python.
Target Field
It is better to know beforehand what is the target field desired to be joined after learning a particular language.
Python is a very popular and readymade language that comes equipped with many libraries. Also, many tech giants and small enterprises have adapted Python language and utilize it for their systems. Thus, learning Python makes it industry-ready.
But if the learning intention is to get involved in academia, R is favored over Python. R is a robust statistical tool that has been accepted by many academia. Also, very few industries have adapted R in their systems.
Learning Objective
Considering the objective of learning the language should be made clear before getting hands-on with any of the software.
The field of statistical learning requires the heavy manipulation and visualization of data sets. The subpart of statistics, statistical learning deals with regression analysis, uncertainty, and precision. Developed to suit the statistical needs of the programmer, R language is the apt software when heavy statistical analysis and learning is the major learning objective.
Python is a ready-made and more implementable programming language. This is best suited for Machine Learning goals as the language has the potential to get easily implemented on ML models.
Statistical Analysis or Software Engineering
For statistical analysis, as already mentioned, R is the best choice. The R programming software fits appropriately in the research and analysis environment.
But if engineering is to be considered, the Python programming language is at par with the engineering models. With the help of some other programming languages like C/C++ or Java, Python is given more power for developing more efficient codes and programs, that are more efficiently implementable on the engineering systems.