Anyone interested in Machine Learning often face this dilemma, which language to learn for Machine Learning: Python or R? If you already know at least one of this language, you are ahead of so many! I dont know either. As an economist I am well trained in Stata, SAS and Eviews; but not in Python or R. Many of us could be in same situation like me. By the way, SAS has a data miner app (SAS Enterprise Miner) that can be used for machine learning too. I am bit familiar with Matlab and it has very powerful and GUI machine learning app as well. Both of these software’s are very expensive, not viable for many students.
I prefer to go with Python or R: they are free, open source, very strong and active community where you can get most of the problems solved very easily. But these are not as user friendly as SAS or Matlab. I will update a list of pros and cons for each here.
Difference between R and Python
|Objective||Data analysis and statistics||Deployment and production|
|Primary Users||Scholar and R&D||Programmers and developers|
|Flexibility||Easy to use available library||Easy to construct new models from scratch. I.e., matrix computation and optimization|
|Learning curve||Difficult at the beginning||Linear and smooth|
|Popularity of Programming Language. Percentage change||4.23% in 2018||21.69% in 2018|
|Integration||Run locally||Well-integrated with app|
|Task||Easy to get primary results||Good to deploy algorithm|
|Database size||Handle huge size||Handle huge size|
|IDE||Rstudio||Spyder, Ipthon Notebook|
|Important Packages and library||tydiverse, ggplot2, caret, zoo||pandas, scipy, scikit-learn, TensorFlow, caret|
|Disadvantages||Slow High Learning curve Dependencies between library||Not as many libraries as R|
Final verdict: If you want to learn only one language for machine learning, it is Python!