A Complete Tutorial to Learn Data Science with Julia from Scratch

The above line tells a lot about why I chose to write this article. I came across Julia a while ago even though it was in its early stages, it was still creating ripples in the numerical computing space. Julia is a work straight out of MIT, a high-level language that has a syntax as friendly as Python and performance as competitive as C. This is not all, It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. But this article isn't about praising Julia, it is about how can you utilize it in your workflow as a data scientist without going through hours of confusion which usually comes when we come across a new language. Before we can start our journey into the world of Julia, we need to set up our environment with the necessary tools and libraries for data science. Jupyter notebook has become an environment of choice for data science since it is really useful for both fast experimenting and documenting your steps.