One of the most common questions I get asked is, “Should I learn R or Python?”. My general response is: it’s up to you! Both are popular open source data platforms with active, growing communities; both are are highly sought after by employers, and both have a rich set of capabilities for working with data. It really depends most on your interests and the kind of employer you want to work for.
If your interests lean more towards traditional statistical analysis and inference as used within industries like manufacturing, finance, and the life sciences, I’d lean towards R. If you’re more interested in machine learning and artificial intelligence applications, I’d lean towards Python. But even that’s not a hard-and-fast rule: R has excellent support for machine learning and deep learning frameworks, and Python is often used for traditional data science applications.
One thing I am quite sure of though: neither Python nor R is inherently better than the other, and arguments on that front are ultimately futile. (Trust me, I’ve been there.) Which is better for any given person depends on a wide variety of factors, and for some, it may even be worthwhile to learn both. Brian Ray recently posted a good overview of the factors that may lead you towards R or Python for data science: their history, the community, performance, third-party support, use cases, and even how to use them together. It’s great food for thought if you’re trying to decide which community to invest in. The cartoon below is taken from that post, and sums up things quite nicely.
And what about me, you ask? My needs generally fall on the statistics / data science end of the spectrum, and my interest in deep learning has been served well by the keras support from RStudio. I’ve been using R for a long time, and I just haven’t found anything yet that I, personally, has led me down the Python path. But your needs may vary from mine, and the article linked below is a good guide to that decision.
Brian Ray: Python vs (and) R for Data Science