Julia’s Role in Data Science

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JuliaI’ve been hearing about the Julia programming language for data science applications for about a year now, but I haven’t taken the plunge yet in using it for myself (since I’m such an R devotee). But after reviewing an informative Julia summary from the recent Strata Conference + Hadoop World, I think it’s time to take a closer look.

Since its first public release in February 2012, the Julia programming language has received a lot of hype. This has led to some confusion about the language’s current status. The summary makes clear where Julia stands and where Julia is going, especially in regard to Julia’s role in data science, where the dominant languages are R and Python. The developers are working hard to make Julia a viable alternative to those languages, but it’s important to separate out myth from reality.

Here’s how the developers describe why they created a new language:

We want a language that’s open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that’s homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy. We want it interactive and we want it compiled.

Sound interesting? If so then read the summary written by John Myles White (co-author of one of my favorite machine learning books).

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