<p>The field of data science is constantly..</p>

An introduction to Julia!

The field of data science is constantly evolving and new tools are being developed at a rapid
pace to meet the needs of data scientists. From specialised programming languages like R,
to machine learning frameworks like TensorFlow and PyTorch, there are a wide variety of
tools available to help data scientists work more efficiently and effectively.

Currently 23rd on the PYPL index, Julia has been around for over a decade. There is little
doubt as to whether Python and R will continue to reign supreme over the data science
space. However Julia’s growth in popularity, with 45 million downloads as of January 2023, a
growth of 13,000% since 2016 (Julia Annual Growth Report) and being placed 12th in
Redpoint’s Open-source top 25, it would be remiss to not understand what the language
has to offer.

What is Julia?

Julia is a high-level, high-performance, dynamic programming language designed to address
the needs of numerical and scientific computing, data analysis, and visualisation. It was first
introduced in 2012 by a group of computer scientists and mathematicians from MIT, and has
since gained popularity among researchers and developers in various fields, including
physics, engineering, finance, and machine learning.

The Key benefits of Julia

It’s fast. In its documentation, Julia claims to be approaching the performance of C, which is
rarely heard of for a high-level programming language. When implemented properly, it can
be seen that Julia hugely outperforms R and Python in terms of speed, shown in its micro
benchmarks. In 2017, Julia became the first high-level language to join the Petaflop Club
after managing to catalogue 188 million stars, galaxies and other astronomical objects in just
14.6 minutes. This level of speed has been achieved through its just-in-time (referred to as
JIT) compiler and its built- in parallel computing support.

It’s flexible. Its ability to seamlessly integrate with other programming languages, such as
Python, R, and C/C++, allows users to take advantage of existing code libraries without
having to rewrite them in Julia. In addition to a growing ecosystem of over 8000 packages, it
can be argued to combine the strengths of the major players within the data science field.

Should you learn Julia

Overall, Julia is a modern programming language that offers a unique combination of ease of
use, speed, and flexibility, making it an excellent choice for a wide range of applications in
scientific computing and data analysis. It has effective use-cases in machine learning and
statistics to supercomputing and financial modelling.

However, it is young. Its ecosystem is still growing, and one should be wary of learning it
instead of Python. However, for those already proficient in the major languages, Julia is an exciting tool to learn, to prepare for the future as we deal with exponentially more and more data.

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