Big data has become an industry unto itself, with many companies now looking expressly for data scientists and data specialists. But up until recently it’s been fairly difficult to actually parse, analyze, and control big data; big data is, by its very nature, cumbersome and disparate information sets that cannot be analyzed through traditional means. Though Python and R have been historically used for the analysis of big data, a third contender is also becoming popular: Julia.
What Is Julia?
Julia isn’t an entirely new language. In fact, it was first introduced back in 2012. But languages tend to be adopted slowly. Julia is designed as a general purpose language that also makes scientific computing easier. In that aspect, it’s more similar to Python than to R. Julia has a multitude of features that are designed to make high-performance numerical computing much easier. It also includes an interactive session shell through which code can be tested very quickly. Though Julia is currently a fairly obscure language, it’s also a very powerful one, which integrates many of the best aspects of Python and R.
Why Is Julia Better Than Python or R?
Python is a more general purpose language than R, while R’s specialization is what gives it its power. Essentially, the comparison between Python and R is the comparison between a Swiss Army knife and a kitchen knife. Programmers often learn Python when they want a general purpose language that is good for big data analysis. Programmers often learn R when they want a big data analysis language.
But where does Julia fit in? Julia is faster than Python, which can be staggeringly important for larger big data applications. Julia also makes it faster to program and deploy code, though the code may be a little messier. This means that Julia may not be as well suited to general purpose applications as Python, but it also gives Julia a lot of agility in terms of rapid fire big data analysis. Code that has to be modified to account for new data sets (and has to be modified quickly) may be best created in Julia. This is very similar to the reason why PHP became the leading back-end language for web design; though the code is often loose, it’s fast and it works.
Julia is also versatile. It makes it easier to move between high-level code and assembly functions. When compared to R, it is easier to learn; R is traditionally considered to have a far greater learning curve than either Julia or Python. Thus, Julia manages to excel in the specific field of data science because it is an agile and flexible language.
How Can You Learn Julia?
As with many other languages, Julia has an extensive set of documents and lessons available online. Julia is very easy to experiment with and get started with, so most data scientists will be able to learn the language simply by jumping in.
Julia isn’t a perfect language. It does suffer from a lack of libraries and support because it is so obscure. Further, there are fewer employers who may be looking for it. But employers who are looking for Julia will also be selecting from a limited pool of candidates. If you’re looking for a job within data science or want to learn more about the direction data science careers are heading, take a look at the IT job listings at Software Specialists.
