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Abstract:
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The R project has been extremely successful in providing a language and environment for data analysis and graphics, to the extent that R is accepted as the "lingua franca" of statistical computing. Other languages, such as Python and Matlab/Octave, are also popular in the more general field of data science. The Julia language is a relative newcomer to the field, not yet having reached the milestone of a 1.0 release. Having been developed very recently, Julia incorporates many modern technologies including Just-In-Time (JIT) compilation of functions and methods. Relative to established languages, the infrastructure (available packages, integrated development environment) for Julia is incomplete but growing. Switching from an established language to a new language like Julia for day-to-day work is difficult and time-consuming. It can also be worthwhile. I will describe my experiences doing so.
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