Knowing which programming language to use for machine learning projects is a question that many data & analytics professionals face. After having worked with different tools available for implementing financial advanced analytics projects during many years, I usually answer this question with a determined “It depends”. It depends amongst others on the framework available in your organization, on your projects, on your challenges and on your available resources.
So instead of giving you a clear-cut answer when there is none, I’d like to provide you with an overview of the pros and cons of each programming language in order to help you make the best choice within your respective circumstances.
It may very well seem to you as if there was no tangible difference between Python and R. My answer to this is similar to what I mentioned above. The differences may not be major, but one point in the matrix may very well be so important in your specific circumstances that the choice of programming language will be an obvious one for you.
Or it may be a more difficult choice, as it was the case when we used this matrix for one of our in-house machine learning projects to determine which programming language to choose. If you are curious to know what our decision was: We opted for R as a general solution, but we will still offer the option to work with Python, depending on the specific project. This perfectly illustrates my general answer from above – “it depends” which programming language may be the best choice for your project.
What are the challenges you face when choosing a programming language for your machine learning projects? I’d be pleased to receive an email with your comments and experiences.