Xjyuk

I just finished a 1-year Data Science master's program where we were taught R. I found that Python is more popular and has a larger community in AI.

Is it worth for someone in my position to switch to Python and if yes, why? Does python have any game changing features not available in R or is it just a matter of community?

Of course, this type of questions will also lead to primarily opinion-based answers. Nonetheless, it is possible to enumerate the strengths and weakness of each language, with respect to machine learning, statistics, and data analysis tasks, which I will try to list below.

R

Strengths

• R was designed and developed for statisticians and data analysts, so it provides, out-of-the-box (that is, they are part of the language itself), features and facilities for statisticians, which are not available in Python, unless you install a related package. For example, the data frame, which Python does not provide, unless you install the famous Python's pandas package. There are other examples like matrices, vectors, etc. In Python, there are also similar data structures, but they are more general, so not specifically targeted for statisticians.




• There are a lot of statistical libraries.

Weakness

• Given its purpose, R is mainly used to solve statistical or data analysis problems. However, it can also be used outside of this domain. See, for example, this Quora question: Is R used outside of statistics and data analysis?.

Python

Strengths

• A lot of people and companies, including Google and Facebook, invest a lot in Python. For example, the main programming language of TensorFlow and PyTorch (two widely used machine learning frameworks) is Python. So, it is very unlikely that Python won't continue to be widely used in machine learning for at least 5-10 more years.




• The Python community is likely a lot bigger than the R community. In fact, for example, if you look at Tiobe's index, Python is placed 3rd, while R is placed 20th.




• Python is also widely used outside of the statistics or machine learning communities. For example, it is used for web development (see e.g. the Python frameworks Django or Flask).




• There are a lot of machine learning libraries (e.g. TensorFlow and PyTorch).

Weakness

• It does not provide, out-of-the-box, the statistical and data analysis functionalities that R provides, unless you install an appropriate package. This might be a weakness or a strength, depending on your philosophical point of view.

There are other possible advantages and disadvantages of these languages. For example, both languages are dynamic. However, this feature can both be an advantage and a disadvantage (and it is not strictly related to machine learning or statistics), so I did not list it above. I avoided mentioning opinionated language features, such as code readability and learning curve, for obvious reasons (e.g. not all people have the same programming experience).

Conclusion

Python is definitely worth learning if you are studying machine learning or statistics. However, it does not mean that you will not use R anymore. R might still be handier for certain tasks.