A young programming language for machine learning is on the rise and could be soon gunning for Python.


Of the many use cases Python wraps, data analytics has become perhaps the largest and most significant. The Python ecosystem is laden with libraries, tools, and applications that make the work of scientific computing and data analysis speedy and suitable.


But for the developers behind the Julia language — intended specifically at “scientific computing, machine learning, data mining, large-scale linear algebra, distributed and parallel computing”. It is a fresh approach to technical computing. But Python isn’t fast or convenient enough. It’s a trade-off, good for some parts of this work but dreadful for others.


Now the question arises - What is Julia language?


Created in 2009 by a team of four-person and unveiled to the public in 2012, Julia is destined to address the drawback in Python and other languages and applications used for scientific computing and data processing.


The official website describes Julia as: “A high-level, high-performance dynamic programming language for numerical computing. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. Julia’s Base library, largely written in Julia itself, also integrates mature, best-of-breed open source C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing. In addition, the Julia developer community is contributing a number of external packages through Julia’s built-in package manager at a rapid pace.”


Here are some of the ways Julia implements:


  • It is a Compiled language and not interpreted which makes it speedy. Julia is just-in-time (JIT) compiled using the LLVM compiler framework. And it is true to the fact that Julia can draw near or match the speed of C language; good performance, approaching that of statically-compiled languages like C.


  • It uses clear-cut but useful syntax. Julia’s syntax is similar to Python’s—terse, and at the same time expressive and powerful than the Python language.


  • It covers dynamic typing with static type benefits. You can specify types for variables, like “unsigned 32-bit integer.” But you can also create hierarchies of types to allow general cases for handling variables of specific types—for instance, to write a function that accepts integers generally without specifying the length or signing of the integer. And, finally, you can do without typing entirely if it isn’t needed in a particular context.


  • It works well with Python, C, and Fortran libraries and makes them just a call away. Julia can also interface directly with external libraries written in C and Fortran. It’s also possible to interface with Python code by way of the PyCall library and even share data between Python and Julia.


Julia supports Meta-programming. Julia programs can generate other Julia programs, and even modify their own code, in a way that is redolent of languages like Lisp.