In this tutorial you will learn about the Julia Introduction and its application with practical example.
Julia is an open-source, multi-platform, high-level, high-performance programming language widely used for statistical computing, data analytics, scientific research, data modeling, graphical representation and reporting. It is an open source programming language mainly designed for high performance scientific and numerical computation. Julia is dynamically typed programming language that support multiple dispatch, parallel and distributed computing. Julia has an LLVM-based JIT compiler which gives high performance without the typical need of separate compilation.
Julia incorporates various key advantages of many other high level programming languages such as C, C++, Python, and Ruby etc. Julia provides the just-in-time compiler that gives it major speed boost over other compiled languages such as C++ and Fortran. It incorporates the dynamism of many other high-level languages, such as Python or Ruby. Julia incorporates mathematical notations similar to many scientific computing languages like Matlab, R, Mathematica, and Octave. Julia provides math-friendly syntax makes it easier for non-programmers to learn and understand. Furthermore, Julia provides the statistical ease of R and the general usage of Python. Combing all these features makes Julia an incredibly powerful language.
- Julia is Open source and free under MIT license
- Julia is Compiled, not interpreted, makes it faster
- Julia is inherently designed for distributed and parallel computing
- Julia provides Automatic memory management mechanism
- Julia is interoperable with other programming languages such as Python and C
- Julia is dynamic typed programming language
- It has easy to learn straightforward and math friendly syntax
- It has in-built package manager
- It comes with a powerful interactive command line tool
- It is highly extensible