6 Reasons Why Non-Data Scientists Should Also Learn Julia in 2021

7 min readFeb 17, 2021

The Julia programming language isn’t just for data scientists

Often touted as the new Python or R, the Julia programming language is taking the data science community by storm.
However, to only view Julia as a programming language for data scientists would be a huge missed opportunity for software, web, and app developers who are looking for an easy-to-learn language that offers more benefits than just number crunching.
Let’s take a look at six reasons why Julia should be added to the toolboxes of non-data scientists in 2021.
1. Julia Is a General-Purpose Programming Language
Julia initially gained its popularity in the data science community as an alternative to Python and R. While Julia is a general-purpose programming language that lacks specialized features for a particular area of development, it became particularly known for its ability to handle scientific and numerical computing. With that being said, its ability as a language doesn’t stop where data cleaning and analysis start.
Julia extends its usefulness to non-data scientists as a language that can be used to integrate artificial intelligence, machine learning, and automation into non-data science-related projects. As these features become even more mainstream for companies to implement, Julia will be the powerful language at the forefront of development.
2. Julia Is a Dynamically Typed Language That Also Uses the Best Parts of Statically Typed Languages
Julia is an incredibly efficient language, as it combines the best parts of dynamically and statically typed languages for a powerful user experience.
Considered to be a dynamic language, Julia also implements the advantages of static type systems by allowing users to indicate the specific types of certain values.
Types are omitted by default, which allows values to be of any type. Much like in Python, functions can be quickly created in Julia without ever explicitly using types. However, unlike in Python, when more specificity is required, explicit type annotations can be added to previously untyped code.
This option to statically type variables makes it easy to organize code and is also part of what makes Julia a super-fast language.
3. Julia Is a Fast Language
Just how fast is the Julia programming language anyway?
Performance is often one of the top reasons why developers look to new languages, so it’s no wonder why many have been looking to switch from the archaically slow Python to a quick, modern variant. Where developers would often have to choose between a user-friendly language (such as Python) or a speedy compiled language (such as C), they now have the best of both worlds in Julia.
In other words, Julia has solved the two-language problem where developers first prototype their code in a user-friendly language and then rewrite it in a quicker language.
Compared to Python, Julia takes the cake when it comes to speed thanks to its just-in-time compilation and type declarations.
4. Julia Uses a High-Level Syntax, Making It Easy for Developers of All Backgrounds To Learn
The high-level style syntax that made Python so popular with first-time programmers is now making Julia an easy-to-learn alternative.
Developers who are familiar with high-level, dynamically typed languages will easily pick up Julia, as it has conventions similar to those of Python. While it’s a new language, many Julia tutorials are now becoming available through a wealth of knowledge in the open source community.
Currently, MIT offers a popular MOOC where computational thinking is taught using Julia.
5. Julia Makes Data Analysis Easy
Made for dealing with scientific and numerical computations, the Julia programming language has been taking the data science world by storm, as it offers a powerful alternative to more mainstream languages such as Python, R, and MATLAB.
While data analysis isn’t often in the wheelhouse of software developers, it doesn’t hurt to have some simple data science skills in your back pocket. With exabytes of data being produced every day, data analysis is becoming a hot skill for anyone to have.

While Julia lacks many of the packages, frameworks, and libraries found in a mature language such as Python, most data science activities — including data cleaning, analysis, and visualization — can be accomplished with little fuss. Furthermore, as mentioned above, Julia is a great language to use for developing artificial intelligence, machine learning, and automation systems to make the data analysis process a smooth one.
6. Julia Is an Open Source Language
You’re generally guaranteed a good debate when you enter a room full of programmers and open the floor to the age-old argument of open source vs. licensed programming languages.
However, the advantages of Julia being an open source language far outweigh the disadvantages:
The language’s source code can be modified to suit the requirements of a particular project.
Programmers from around the world can collaborate to fix bugs and manipulate the code to make it more efficient and easier to maintain.
Open source programming languages are free to try before implementation, unlike licensed languages that may have a short trial period.
Open source languages are generally more secure than their licensed counterparts.
Final Thoughts
For all of its advantages, Julia still harbors some disadvantages that everyone should be aware of before picking up this language:
Julia is a very young language. Compared to Python, which has been around for decades, Julia is a young programming language with few features. Because of this, other languages have much larger open source communities and more options for packages and features.
Instead of array indexes starting at 0, Julia has its array indexes start from 1. This is generally hard for developers to wrap their heads around. The developers behind Julia chose this design, as it’s a logical way of doing things for mathematical calculations, in which case starting arrays at 1 makes sense.
As a young language, it’s tough to determine where Julia will be in a few years. As far as many in the data science community are concerned, the Julia programming language will remain a key alternative to the traditional Python, R, and MATLAB.
However, for many of the reasons mentioned above, Julia could become a prevalent and effective language outside of the data science realm. With a little something for everyone, Julia could soon be seen in many different development areas, including web, software, and app development.
The question is, will enough developers be willing to give it the shot it deserves?

6 Reasons Why Non-Data Scientists Should Also Learn Julia in 2021

Written by scric strak