Why Is Julia Still a Relatively Obscure Language in People’s Minds?

Being one of the first supporters of this programming language, at least for data science, it saddens me to talk to people in the tech industry and find out that they have never heard about this language. It’s even worse when this ignorance of it comes from people involved in data science and A.I., people who should have at least tried it. With Julia becoming more and more relevant among programmers (see the latest newsletter to get an idea), it seems like an oxymoron that it is still relatively obscure in people’s minds, particularly people involved in data science.

One reason why this happens is that the institutions that deliver data science know-how (I wouldn't call it education yet, since they don’t cover soft skills or the whole mindset aspect of the craft) are ignorant of Julia. This makes sense in a way since their all-knowing instructors are experts in the mainstream languages of data science, namely Python, Scala, and R. So, if the people you trust to teach you about data science are rookies in Julia, they’ll probably not even mention it, just like people in computer science don’t talk much about Quantum Computing, or security experts about how conventional security systems are sitting ducks when it comes to code-breaking that could be performed by QC systems.

Another reason is that people learning data science are overwhelmed with the numerous technologies and tools of the field. As a result they take a pick on what to learn and they tend to gravitate towards the tools that have the most literature around them. Also, these tools tend to have been tried and tested the longest, so they are more risk-free. Since there are enough risks in getting into a new field, people tend to want to minimize additional ones if they can help it, so they give Julia a pass.

Moreover, Julia has gained a lot of traction in academia, since many researchers are open-minded enough to give it a try, while they are also fed up with the (oftentimes proprietary) systems they use. Matlab may be great if someone else pays for the license to use it, but it’s doubtful that you’d pay for it yourself after your studies are over, especially if you end up with a measly salary as a junior data science researcher. Because of all this, Julia may have started to appear as an academic programming language to some people, something that is good for researchers but not for people in the real world.

Of course, all these ideas about the Julia language are nothing but misconceptions about it. After all, it doesn't try to replace any other language, since it is highly compatible with many other programming languages, such as Python, R, C/C++, and even Java. So, if you feel like you have to choose between Julia and the language you are comfortable with, then probably you are gravely misinformed about the language. There is a reason why the company that develops it and supports it is doing so well. There is also a reason why many companies are using it (though they don’t always talk about it, for obvious reasons).

So, if you still find Julia an obscure programming language for data science, you may want to divert your skepticism towards those who try to ignore it, for their own reasons. Maybe those people have formed views about it based on ignorance, rather than experience with it. Perhaps if you take the time to learn it and use it a bit, you’ll change your mind about it.