MATLAB: MATLAB is a language that is globally used for performing the high-level technical computation. The term MATLAB is used for Matrix Laboratory, which facilitates us with an interactive environment to perform reports and data analysis. It also allows us to implement computing algorithms, plotting graphs, and other matrix functions.
Some features of MATLAB:
- It manages array and matrix problems.
- It helps to solve complex algebraic equations.
- MATLAB is used to analyze data and plot graphs.
- It can also process and communicate with equations of signals.
Julia: Julia is a programming language that is developed for performing machine learning and statistical computation. This language is a functional model that combines many features of other programming languages. It is developed by Viral Shah, who was also engaged with the Aadhar project. This language also supports solving a complex computational problem and algorithmic problems.
Some features of Julia:
- Julia is a compiled language as its speed is fast as compared to interpreted languages.
- It is designed for specifically linear algebra.
- It is a versatile language for machine learning.
- It has easy and understandable syntax.
Following is a table of differences between MATLAB and Julia:
S.No. MATLAB Julia 1. It is a high-level programming language that is used for performing mathematical computing. It is a language focused on scientific computing, data analysis, and statistical programming. 2. MATLAB is developed by Math Works. Julia is developed by Julia Computing. 3. This language is written in C, C++, and Java. This language is written in Julia, C, and R. 4. The file saved is with extension ‘geeksforgeeks.m’. The file saved is with extension ‘geeksforgeeks.jl’. 5. It is not an Open-source language It is an Open source programming language. 6. MATLAB focuses on data analysis. Julia focuses on Scientific computation. 7. MATLAB code gets complex when matrix functions get executed. Julia has enriched the library from R, Python to support matrix functions. 8. It is used for Bigdata, Artificial Intelligence, and Data Analysis. It is used for Machine Learning, Data Analysis, and Parallel Computing.