Note: In this post we’ll compare and contrast different programming languages. Everything discussed should be taken with a grain of salt. There’s no single programming language which solves all the problems in an elegant and performant way. Every language has its up- and downsides. Swift is no exception.
Entering the Data Science and Machine Learning world there are various programming languages and tools to choose from. There’s MATLAB, a commercial programming environment which is used across different industries and usually the tool of choice for practicioners with a heavy Math background. A free and Open Source alternative is the R project, a programming language created in 1993 to simplify statistical data processing. People working with R usually report enjoyment as R is “hackable” and comes bundled with different math modules and plotting libraries.
A more recent incarnation is the Julia scientific programming language which was created at MIT to resolve the issues older tools such as MATLAB and R struggled with. Julia cleverly incorporates modern engineering efforts from the fields of compiler construction and parallel computing and given its Open Source nature it has gained a lot of industry-wide adoption when it reached
v1 maturity in 2018.
Python as the de-facto standard
If you’re doing some more research to find the most used programming language in Data Science and Machine Learning you might be surprised to see a language which wasn’t built from the ground up for scientific computing: Python.
The Python programming language was created by Guido van Rossum in 1989 to help bridge the gap between Bash scripting and C programming. Since then Python took the world by storm mainly due to its flat learning curve, its expressiveness and its powerful standard library which makes it possible to focus on the core problems rather than reinveing the wheel over and over again.
Funny tangent: Open up a shell, run the Python interpreter via
python and enter
import this or