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 which can be popular today, Julia is radically different by many comparisons. This is very the case in paradigm, or how the varieties of the language (which hold the info,) work with functions or methods within the language. There are plenty of different solutions which were proposed for this, and each language tends to take a seat in a single category — though not necessarily firmly. Most up-to-date languages have bridged multiple programming concepts through these paradigms, and this makes most up-to-date programming languages multi-paradigm. Under this umbrella, the Julia language would even be included.

Although Julia is a multi-paradigm programming language, it’s inbuilt a singular paradigm centered around multiple dispatch. This just isn’t to say this paradigm is entirely novel, the idea of this paradigm is present in the Standard Meta Language (SML). I actually, due to Julia, went back and tried that language ages ago — it was a really interesting experience, and if you happen to would love to read more about what transpired with that here’s a link to that article:

There are quite a lot of really cool things that Julia can do which can be entirely unique to the language. This paradigm and its feature set brings quite a lot of excitement and recent capabilities to a programming language, but in addition creates quite a lot of hurdles. Benefiting from these complexities can mean quite a bit in terms of Data Science. With capability comes complexity, and with certain nuances of Julia which can be somewhat unique it may be tricky for users to get a full Julia experience. There may be quite a bit to learn, and Julia is a seriously awesome language to work with, so it’s price benefiting from!

So far as Data Science goes, Julia is the brand new kid on the block, but its community…