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What is the Best Programming Language for Data Science?

The debate over which programming language is best has raged through the ages – or at least, for the past couple of years. So overworked and seemingly endless have the disagreements become among computer programmers in recent years that humorists from the field sometimes claim that Socrates preferred Python while Plato was an advocate of Julia.

But generally speaking – and jokey historical claims aside -- Julia is faster at solving complicated mathematical and statistical problems and doesn’t require an intermediary language, while Python has the largest user base. Additionally, the availability of Python's extensive open source libraries make it more broadly applicable – at least for now.

Let’s take a look at the relative merits and challenges of both.


Python, released in 1991, is a freely available, open-source programming language that is the darling of the scientific community; it has several libraries dedicated to specific areas of science. Due to its readability and clear syntax, designers can express content in fewer lines of code compared to other popular languages.

Python also closely resembles the English language, using common small words while having strict punctuation rules to increase the readability among users. As a result, Python is beloved by many developers because it makes coding faster and easier. Its code can be interpreted by many operating systems, which allows it to be a general purpose programming language.

As it has been around for over 20 years, an enormous amount of Python code has been written for a plethora of industries and tasks. Its open-source nature means that just about any use you can come up with – from scientific calculations to server automation – is readily available.

Not surprisingly, a number of universities acknowledge the importance of Python by dedicating teaching resources to it. For instance, Lewis University’s online Master of Science in Data Science program incorporates Python – along with other languages -- as a means of providing students with background and insight into key mathematical and computer science issues involved in the analysis of Big Data.


Julia is a more recent addition to the programming toolkit. It is an MIT-licensed open- source programming language released in 2012 in response to a challenge facing many developers—such as the fact that some development ideas require multiple programming languages, adding complexity when writing code as well as in debugging and patching. For those using Python, for example, some lines of code may need to be written in intermediary programming languages like C or C++ because Python is unable to process the information fast enough. Julia does not require an intermediary language.

Julia's creators took the clean syntax of Python and built onto that. Basic benchmarks run 30 times faster than in Python and slightly faster than C and C++. Julia can handle complicated mathematical and statistical problems relatively fast, too. A user can start 100 processes and run them across different machines to speed up computation time.

Choose wisely

On the surface, Julia would seem the better choice, as it makes use of many of the best features of Python and adds to them. But here is the catch. Python’s longevity and wealth of libraries has led the likes of Google, NASA, Intel, AMD, YouTube, Mozilla, Dropbox and Facebook to build platforms on it. Julia, on the other hand, is far newer, has a more modest library collection and could be considered more of a work in progress, though rapid-fire development is adding libraries constantly. So as Plato said, “Opinion is the medium between knowledge and ignorance.” Therefore, the best answer is to learn and use both, and other languages as well. Lewis University’s online M.S. in Data Science, which, covers Python, R, SRS and many more, is one of the best places to survey the field. These languages are becoming ever more important, as the growing adoption of Big Data worldwide drives the demand for data scientists with a firm grasp of advanced programming.