Some of the features of Julia are:
It is Open Source on GitHub.
Combination of power, simplicity, and efficiency.
Has the speed of C.
Looks like Python.
Easy for statistics like R.
Interoperability: Capability of two different programming languages to natively interact as part of the same system.
Dynamic.
General optimally typed.
Support for Unicode, including but not limited to UTF-8.
The feature I love most is that it looks like Python. Coming from a Python background, it's quite easy to learn and understand the language within a few hours. Below is a countdown function written in Julia and Python.
# julia
function countdown(n)
if n ≤ 0
println("Blastoff!!")
else
println(n, " ")
countdown(n - 1)
end
end
countdown(5)
# python
def countdown(n):
if n <= 0:
print("Blastoff!!")
else:
print(n, " ")
countdown(n - 1)
countdown(5)
Julia is mainly used for Scientific computing. Some non-scientific computing uses are:
Backend Development.
Visualization.
Data Science.
Machine Learning.
Parallel Computing.
There are several opportunities that come with the use of the Julia language. Each opportunity is connected to a use case of the language and some of these include:
Machine Learning Engineer.
MLOps Engineer.
Geospatial developer.
Researcher.
The Julia ecosystem has experienced massive growth in recent times as more developers discover about the language and its utility. Getting started with Julia is easy and communities exist ready to offer assistance. Currently, the official communities are on Slack and Discourse.
I've only just begun to scratch the surface of the programming language but with only a little study and research, I've been able to gather knowledge on some excellent Julia features and opinions. It is a modern programming language that prioritizes efficiency and usability.
If you're looking to try out something new, Julia is definitely worth your time.
Thank you!