How often one working in the field of data science is faced with the question, What is better
for data science, Python or R?
Well, there can be no right or wrong answer to this question. Depending upon the data
analytical usage and costs both serve the purpose pretty well. However, if you are still
confused about which choice to make, this article will help you understand the difference
between Python and R for data science. The differences highlighted in this blog will help you
get a vivid insight into the place of R and Python in the data science and statistics world.
The purpose behind developing R was to formulate a language that introduced an interface
which was highly user-friendly and a better way to do data analysis, statistics and graphical
models. During its inception it was used primarily in Academics and Research. Over time
this language was adapted by the enterprises as well. The Enterprise world is, however, still
discovering its usages. As of now, it is amongst the fastest growing statistical languages in
the corporate world.
Strengths of R:
Amongst its numerous strengths is the Huge community support. The community provides:
1. Mailing lists.
2. User-contributed documentation.
3. An active Stack Overflow group.
4. CRAN repository of curated R packages.
The main objective behind the development of Python was to enhance productivity and code
readability. It is preferred by the Programmers who wish to choose a career in data analysis.
Programmers applying statistical techniques are the prominent users of Python. Basically, it
is preferred by those working in an Engineering environment.
Strengths of Python:
1. It is a very flexible language.
2. The learning curve for Python is low
3. It is developed to enhance productivity and readability.
4. It is a general purpose language, with a strong community (though scattered)
5. Many innovative Data Science Applications trace their origins to Python.
It is very difficult to choose between the two for the purpose of data science as R is confined
only to Data Science however Python is a general language. Both these languages definitely
have a different approach to data science.