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Friday, March 11, 2016

Grammar of Common Data Science Tools

In KDNuggets:  Nice look at the practical differences between R and Python.  Now working with groups that often to use both.   As it has always been, people use what they are most familiar with,  trained in, but then when things need to interact with real world expectations like speed or maintenance, things must be reconsidered.  Also makes the point that R is best for initial analysis, experimentation and graphics, Python for delivering specific deliverable systems solutions.   Its also hard to create standards when needs and methods are changing quickly. Don't waste time, set some basic standards.

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