Choosing between R and Python!

Nowadays the very basic question that any Data Science beginner asks me is which programming language to build their career into and the obvious choices are R and Python [SAS and MATLAB have almost lost their grip to open source options]. Since I have worked on some of these programming languages, I know the difference and try to guide them well based on success and failures I have faced during my career.

No Language is Superior or Inferior, its about the usecases that they solve. One may be better than another in certain specific areas/domains, that's why its yet surviving.

R is core stats language with numerous built-in packages that helps achieve the ML tasks whether you have to build a Supervised or Unsupervised model or to do Text Processing/NLP. It is widely used at Institutes and in teaching field. However the limitations are that with increased demand for faster and distributed data processing, it falls behind Python in adopting to Parallel processing frameworks like Hadoop/Spark as PySpark is one of the most famous framework to code ML models in Python.

Also lot of research is done in recent times by Python community and numerous packages have been added to it for Machine Learning [ML] and specifically the Deep Learning [DL] domain. Python far exceeds the DL space with the ease of integration with Keras and Tensorflow alongwith other Deep Learning packages.

Supports like Anaconda, Jupyter Notebook and Zeppelin Notebook makes the Python adoption very easy and makes it the better choice for new beginners.

You can make use of Flask and Django in Python for building APIs and exposing your ML models to various frontends and interfaces easily.

Also if you learn python, then there are lot of other paradigms that gets open for you as IoT, Blockchain developers making use of Python for building smarter solutions and delivering cutting edge products that are shaping the future of IT and the way we see or interact with our environment.

So, think thoroughly and adopt Python!