Data Science Is New Oil ! But Which Tool To Master? R Vs Python
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.
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
Here is in depth analysis of popularity trend shown by Python and R within developer community and industries.
Overview of Features of Python and R
To choose one from Python and R we should know about the pros and cons of both of them .
Here are few important of them in aspect to Data Science and Machine Learning.
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
Finally I want to say choose wisely according to your project requirements and preference of your industry.