13 Data Science Things I Learned at JuliaCon 2020

In this article, I will share 13 data science-related things I learned about Julia at JuliaCon 2020. I've grouped my learnings into 4 categories namely machine learning, tools, coding in Julia, and miscellaneous. MLJ.jl is a package from the Alan Turing Institute that serves as an interface to interact with machine learning algorithms in other packages. In addition, it provides functions to do common tasks in a machine learning project such as evaluating models, model stacking and hyperparameter tuning. There was an MLJ workshop run by Anthony Blaom, Thibaut Lienart, Geoffroy Dolphin, Okon Samuel, and Sebastian Vollmer where they demonstrate how to use MLJ to build models on the Iris dataset.