Julia

The philosophy of Julia is to have a general-purpose programming language similar to Python but at the same time has the capability to perform data visualization, machine learning and other data science applications with relative ease. In essence, Julia is meant to be an ecosystem for numerical analysis and computational science.

The maintainers of Julia describe it as being comparable to static programming languages such as C, by letting the compiler guess the type of the data when possible, hence the performance boost. To guarantee that your code will benefit from this performance boost make sure to read through the Performance Tips on the Julia official website.

Did we not mention that Julia is free and open-source? Well, it is. Julia joins a long list of open-source scientific computing software that is growing day by day such as Maxima, SageMath, GNU Octave, FreeMat and so on. However, Julia is still relatively new (first appeared in 2012) thus it is not yet as popular compared to Python or MATLAB for example. 

To know more about the differences between Python and Julia you can read the article Julia vs. Python: Which is best for data science? By InfoWorld.

Julia features

• Free and open-source under the MIT license.
• General-purpose similar to Python, but geared toward scientific computing.
• High-performance that is comparable to static languages such as C.
• A dynamic programming language, but the compiler has the capability to predict the type of data used in the code.
• Ability to call libraries from other languages such as C, Python, and Fortran.
• Full debugger integrated.

Download Julia

To get the latest version of Julia 1.2.0 with a direct and fast download link via Downzen click on the download button on this page or visit the download page on the official website.