2.1 For Non-Programmers

In the first background, we expect the common underlying story to be the following.

Data science has captivated you, making you interested in learning what is it all about and how can you use it to build your career in academia or industry. Then, you try to find resources to learn this new craft and you stumble into a world of intricate acronyms: pandas, dplyr, data.table, numpy, matplotlib, ggplot2, bokeh, and the list goes on and on.

Out of the blue you hear a name: “Julia.” What is this? How is it any different from other tools that people tell me to use for data science?

Why should I dedicate my precious time into learning a language that is almost never mentioned in any job listing, lab position, postdoc offer or academic job description? The answer is that Julia is a fresh approach to both programming and data science. Everything that you do in Python or in R, you can do it in Julia with the advantage of being able to write readable2, fast, and powerful code. Therefore, the Julia language is gaining traction, and for good reasons.

So, if you don’t have any programming background knowledge, we highly encourage you to take up Julia as a first programming language and data science framework.

  1. 2. no C++ or FORTRAN API calls.↩︎

CC BY-NC-SA 4.0 Jose Storopoli, Rik Huijzer and Lazaro Alonso