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Learn With Me: Julia - Introduction (#1)

Learn With Me: Julia - Introduction (#1)

This article is part of the series.

Welcome to Learn With Me: Julia. A series where you can follow me along my journey of learning Julia, Data Science and Machine Learning. This series is heavily inspired by Learn With Me: Elixir, a series by Kevin Peter / The Inquisitive Developer and the format of this post will follow his introductory post for Elixir.

I realised that while there are plenty of resources about Julia already out there, it would be interesting to document my journey in picking up the language and some fundamental data science and machine learning with it.

The Julia community, to a large degree, consists of academics. The level of discourse on the Julia Slack / Zulip is often too advanced for me to understand. Researchers from all kinds of fields, space engineering, bio engineering, mathematics all come together to practice Julia. The 2020 Community Survey nicely shows this:

Slide from the 2020 Julia Community Survey

About You

I'm going to be writing this series for someone who has some programming knowledge and like me wants to learn about Julia. Some familiarity with computer science and algorithms will be useful and you should also have an interest in mathematics as I like to lean into the mathematics of machine learning when I'm ready to explore it with Julia.

For the rest of this section I'll quote Kevin Peter, since it also applies here:

So while this series is not meant for beginning programmers, you don't have to be a master programmer to follow along either. I very much doubt I will be delving into any advanced theoretical concepts or heavy mathematics. I'm aiming for practical stuff that a typical experienced software developer will be able to read and understand. I aim to be easily readable and informative.

If you need a resource on how to get started with Julia and a quick overview of why I chose this language you can read my Getting started with Julia post.

About Me

I left academia six years ago and have since been working with Ruby, JavaScript and Python in a professional setting almost exclusively. In my job I build web application backends and ETL pipelines and work closely with data scientists.

My personal interest in Machine Learning is what's driving me to Julia. Its expressivity over other languages like Python intrigues me and makes me think that it's only going to grow going forward.

Recently I've challenged myself to practice Julia 45 minutes every (week)day as part of a #100daysofcode challenge. I'm 25 days into this challenge and have explored the popular libraries such as Pluto, Plots, Revise and Javis.

You can find more information about who I am on the About page