Data scientists pride themselves on knowing every programming language under the sun, every library available to man and the ability to work in only code like the Zion operators from the Matrix. They are also control freaks who like to tweak and fine tune their models like F1 racing cars. So, with this in mind, how do “Drag and Drop” tools fit within the Data Scientist's toolbox?

With the recent developments in “self-service” technologies servicing a majority of tasks that a Data Scientist will typically encounter, there is much being offered that can significantly speed up how a Data Scientist “wrangles” data, experiments with models and share insight. Combine this with the ease of deploying some of these “self-service” technologies and the ability to remotely access such services over a web-based interface, significant user exposure and efficiency gains are potentially within “easy” reach.

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