introduction
As far as programming languages and their paradigms go, Julia’s take is incredibly unique. Comparing Julia to the slough of other multi-paradigm programming languages that are popular today, Julia is radically different by many comparisons. This is especially the case in paradigm, or how the types of the language (which hold the data,) work with functions or methods in the language. There are a number of different solutions that have been proposed for this, and every language tends to sit in one category — though not necessarily firmly. Most modern languages have bridged multiple programming concepts through these paradigms, and this makes most modern programming languages multi-paradigm. Under this umbrella, the Julia language would also be included.
Although Julia is a multi-paradigm programming language, it is built in a unique paradigm centered around multiple dispatch. This is not to say this paradigm is entirely novel, the basis of this paradigm is found in the Standard Meta Language (SML). I actually, because of Julia, went back and tried that language ages ago — it was a very interesting experience, and if you would like to read more about what transpired with that here is a link to that article:
There are a lot of really cool things that Julia can do that are entirely unique to the language. This paradigm and its feature set brings a lot of excitement and new capabilities to a programming language, but also creates a lot of hurdles. Taking advantage of these complexities can mean a lot when it comes to Data Science. With capability comes complexity, and with certain nuances of Julia that are somewhat unique it might be tricky for users to get a full Julia experience. There is a lot to learn, and Julia is a seriously awesome language to work with, so it is worth taking advantage of!
As far as Data Science goes, Julia is the new kid on the block, but its community…
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