Why I prefer Python over R for Analytics

Analytics is a wonderful industry to be in. I joined an analytics firm last year after my MBA. I loved my job and everything was fine till the day I realized that knowing equations or tools weren’t going to cut it.

To delve deeper into analytics, I needed some knowledge of programming. I shortlisted R and Python for it. The moment I started with R, I knew I was in a deep trouble. Make no mistake, it is a great tool for statistical modelling. However, being a first time programmer, understanding the syntax/logic was paramount. This is where I found it so difficult to use. Adding to my misery were the cryptic error messages and the additional commands needed to trace them.

After about 2 months struggling with R, I shifted to Python. And boy, did it make me happy ! The learning curve was surely not as steep as R. Plus, the errors messages were very specific. I use Python for varied areas in analytics like NLP, hence easier trouble shooting is a must for me. Why should I have to worry about trouble-shooting code when I can think of multiple analytical workflows in the meantime? In short, there are three reasons why I prefer Python over R — easier syntax, intuitive programming logic and proper error messages.

The industry is often split between R and Python and its applications. Companies are increasingly using both in tandem — R for complex statistical modelling and Python for analytics/visualization. However with rapidly developing Python modules like Numpy, Pandas, Scipy and Scikit, I won’t be surprised to see a mass exodus from R environment in the coming years. Your thoughts?

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http://www.themeasurementstandard.com/2015/06/choosing-r-or-python-for-data-analysis-an-infographic/