Although many people nowadays are still skeptical about the prospects of Julia as a data science programming language, the Julia community doesn't seem to pay much attention to this viewpoint. In fact, as the community of Julia users grows and more and more people outside the US use it, its main conference, JuliaCon, is starting to adapt accordingly. This year, for the first time ever, it took place outside Boston, namely in San Francisco. This coming year, it is scheduled to take place in London, UK.
Although JuliaCon has a strong presence in India too, with a version of having taken place in Bangalore, during the Autumn of 2015, JuliaCon has remained primarily an American event. Yet, this coming year it is going to take place in Europe for the first time, something that few people would have imagined a few years ago. If you think about it, however, it makes sense, since lots of European programmers and scientists have been using Julia for a while now. There is even a course on Julia programming from the university of Glasgow, by Dr. T. Papamarkou. This is a Master’s level course, by the way, so you can tell that they don’t take Julia lightly in the UK!
It is also worth noting that the UK has a tradition in Julia, since the majority of the books published on the language, come from a British publisher. Of course, most of them fail to do justice to the language, which is constantly evolving, making it very elusive for anyone who wants to write a book about it (trust me, I have been there!). Yet, it goes to show that there is a lot of interest in learning and mastering it. This is quite remarkable, considering that Julia is an imported technology, while the programmers’ community in general tends to be quite conservative towards new languages.
What does all this mean for data scientist? Well, a lot, if you think about it. Julia is becoming more of an international language, appealing to professionals around the world, while transcending cultural barriers. It’s no longer some fancy toy for tinkerers, nor some novelty for the coding enthusiasts. Even Google has recognized it as a viable alternative to Python, for their Deep Learning framework (TensorFlow), while other frameworks also include it as an alternative (e.g. MXNet). Moreover, although the majority of the talks in JuliaCon used to be about new packages of the language and the new features the Base package has, lately the focus has shifted since many people talk about its applications in scientific projects. Some of these projects are directly related to data science.
Naturally, a single event is not going to change people’s minds about the language. People who are committed to using Python or R, will continue to use their tool of choice, no matter how much traction Julia gets. However, those open-minded enough to try out alternatives, now have no excuse for not giving Julia a shot. After all, this language was made to be congruent with other programming tools, including Python and R (as well as C and Java), so it’s not really a “this or the other” choice here. On the contrary, you can program in Julia and in other languages, as there are several bridge packages available for this purpose. That’s one of the many things you can discuss with other Julia users, if you decide to go to the Julia conference of 2018, in London.
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.