• Julia is a programming language aimed specifically at “scientific computing, machine learning, data mining, large-scale linear algebra, distributed and parallel computing
• It is being said for Julia that this language is faster than Python while performing the above tasks

Julia programming language is being founded by Viral Shah who is also the CEO and co-founder of Julia Computing. He has the credit of working with Nandan Nilekani on Aadhar project. Julia is a programming language that combines the productivity of R, Python with high performance. This language helps the coders to solve complex and challenging computational problems.

What is Julia programming language ?

Julia is multi-paradigm, functional programming language created for machine learning and statistical programming. Although Python is also used for Machine Learning and computations but it is being considered as an object-oriented language while Julia is believed to be based on functional paradigm.

Julia is programming language which is designed to combine the features of many other languages. It has speed of C, dynamism of Ruby, use of macros like Lisp, mathematical notations like Matlab, usable like Python and good is statistics like R and at the same time good in string processing like Perl.

Features of Julia language

1) Speed: Julia is very fast when used and written properly. Julia is a compiled language while Python is interpreted. However unlike other compiled languages which are compiled prior to execution, Julia is compiled at the run time. To accomplish this, Julia uses Just-In-Time(JIT) compiler based on LLVM compiler framework.

2) Versatility: Julia code can interface with C, Fortran and Python using respective libraries. It can interface with Python code using PyCall library. Julia code is universally executable in R, Latex, Python, and C. This means that you can call other languages in Julia as well as Julia code can be executed in other language using libraries. PyCall and RCall can be used to call Python and R libraries.

3) Best for Machine Learning: Julia is designed mainly for computational and calculations. Its can provide best results when dealing with linear algebra.Julia’s operand system is much closer to that of R which is its biggest benefit.

4) Package Manager: Julia’s Package manager Pkg comes loaded with its own REPL(read-eval-print loop) and Julia package from which we can build, add, remove and instantiate packages.

5) Easy syntax: Julia’s syntax is very easy to use and understand much like Python.

From the above discussions, we can say that all the languages might have some advantages and disadvantages over other languages. It depends on what we want to do and what we want to achieve from the code.

References: TowardsDataScience and InfoWorld