Data Science is new oil ! It has enormous potential and is growing rapidly.
Increased data availability, more powerful computing, and an emphasis on analytics-driven decision in business has made it a heyday for data science. According to a report of IBM, in 2015 there were 2.35 million openings for data analytics jobs in the US. It estimates that number will rise to 2.72 million by 2020.
It's hard to know whether to use Python or R for data analysis. And that’s especially true if you're a newbie data analyst looking for the right language to start with.
But it is possible to figure out the strengths and weaknesses of both languages. One language isn’t better than the other—it all depends on your use case and the questions you’re trying to answer: What should I use for machine learning? I need a fast solution, so should I use Python or R?
It is hard to pick one out of those two amazingly flexible data analytics languages. Both are free and and open source, and were developed in the early 1990's — R for statistical analysis and Python as a general-purpose programming language. For anyone interested in machine learning, working with large datasets, or creating complex data visualizations, they are absolutely essential.
Trends And Highlights
Overview of Features of Python and R
So Which One to Choose ?
In the end, the choice between R or Python depends on:
The objectives of your mission: Statistical analysis or deployment
The amount of time you can invest
Your company/industry most-used tool
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