Julia has been a topic of controversy in the previous year, the year that was critical for the language’s future, at least in the data science domain. In the beginning of that year, while working at a small-medium company as a data science contractor, I remember making the argument that Julia is ready for data science and that we should give it a shot. Both the people of that company and the people of a vendor company (a local data science start-up that was acquired by Apple later that year) were very skeptical about this. Claims that “Julia is not data science ready” which floated all over the web seemed to echo in our conversations as well.
Later that year I focused on my book on the language and its applications on data science, a book I had started writing the previous Fall. At that point no-one else seemed to care about Julia in the data science community and the big players in the corporate world that had a say about data science (e.g. Amazon, Microsoft, etc.) didn’t seem to even take notice on this promising technology. Still, I knew that the merits of this language would one day surface in people’s minds as well as in the web. So, I finished the book, got it published, and gave a couple of talks on the language. Even though it was the first book to have ever be written on this topic (focusing on the data science applications of Julia), it was soon followed by another one from another publisher, bearing the same title! Also, a few days before I gave my first talks on the subject, Julia entered the top 50 languages in the TIOBE index for that month (blog article from Julia Computing). Clearly the claim that Julia was not data science ready had started to seem like an opinion of the less informed people.
It was that Fall, about a year after I’d started working feverishly on my Julia book, that Amazon took a very bold step, which I consider to be the tipping point. That Fall, Julia started to rise in the eyes of the corporate world, as Amazon adopted the MXNet deep learning framework, which included Julia as one of the languages that it supported (MXNet article on my blog). The researchers involved in this project even published a scientific article about this, in collaboration with the University of Washington, a very prestigious academic institution that was one of the first ones to popularize data science education through its corresponding programs.
After that point, Julia was officially a fully cloud-supported technology. Microsoft soon joined the game by adopting it in the Azure framework (blog article by a Julia user in Denmark). Even Google decided to support Julia in its Tensorflow deep learning system, which up until then was Python exclusive. It seems that the use of Julia in data science is not a fad after all!
Yet, there are still people claiming that Julia in not a data science language and that language X is the way to go because most people have been using X in the past few years. Perhaps they are right, at least subjectively. Some companies are so conservative that will probably die before admitting that the technology they are using is not the best out there. However, instead of paying attention to them, you can do your own research on the topic and form your own view on the matter. That’s what I did and I never regretted it!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy flair when it comes to technology, technique, and tests.