2019-07-22, 08:30–12:00, PH 111N
This tutorial targets both new and moderately experienced Julia users. After covering the basics and tools for data science, we will delve into topics such as memory management, type stability, and profiling.
We will kick off this tutorial with an introduction to Julia, which should be accessible to anyone with technical computing needs and some exposure to another language. In the first part of the tutorial, we will cover Julia’s syntax, design paradigm, performance, basic plotting, and interfaces to other languages. We hope to show you why Julia is special, demonstrate how easy Julia is to learn, and get you writing your first Julia programs. In the second part of this tutorial, we will introduce you to data science tools for data management and machine learning algorithms and then delve into topics in performance optimization such as type stability and profiling. We will end this tutorial by going over the parallel computing infrastructure in Julia.