It might be time for data scientists to learn a new programming language. Particularly if they have a need for speed. Last week, the lead developers behind the open source programming language Julia announced the 1.0 release of their project. This signals that the language, which is optimized for data analysis and machine learning, is no longer a work in progress. Julia code written in the 1.0 version will still work even when new versions are released--by contrast, code written in version 0.4 was not guaranteed to work under version 0.6.
Nothing is quite so personal for programmers as what language they use. Why a data scientist, engineer, or application developer picks one over the other has as much to do with personal preference and their employers' IT culture as it does the qualities and characteristics of the language itself. But when it comes to Big Data, there are some definite patterns that emerge.
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