One of the perennial points of debate in data science industry has been – “Which is the best tool for the job?“. Traditionally, this question was raised for SAS vs. R. Recently, there have been discussions on R vs. Python.
A few decades back, when R / SAS launched, it was difficult to envisage the possibilities future will offer. And this turned out to be a ‘blessing in disguise’. Because, it made easy for them to focus on one tool!
But today ? The situation is different. Even before deciding what technique they should apply, they fall into the pit of searching for the best tool to perform that particular task. And finally, they get nothing out of it.
The honest answer is that there is no universal winner in this contest. Each tool has its own strength and weakness. A prudent data scientist would diversify his / her repository of tools and use the one appropriate in each situation. In order to do this, it is critical to know the strengths and weakness of each tool, which is what this infographic offers.
Note: You can read the comprehensive version of this article here.
By now, you must have realized, there is no clear winner in this race. Every tool has its own importance and own strength areas. These strength areas provide them the leverage to survive in industries and hence factors defined in the infographic plays a significant role in their evaluation.