Julia is a high-level dynamic programming language that is gaining popularity. The Julia language is designed for scientific computing and offers several attractive features for data science applications. In this webinar, we will make a case for why a data scientist might consider taking a serious look at Julia. We will show code examples and point the audience to further resources.

Goals of this webinar:

To articulate why Julia is attractive for data scientists
To provide an overview of Julia language syntax and design
To provide additional resources about the Julia language and ecosystem

Presented by:

Gregory Farage and Saunak Sen gfarage@uthsc.edu / sen@uthsc.edu Division of Biostatistics Department of Preventive Medicine University of Tennessee Health Science Center Memphis, TN

Resources

Tutorial

Extra

Annual Julia User & Developer Survey 2021 presented by Andrew Claster.
Annual Julia User & Developer Survey 2019 presented by Viral Shah.
Announcing composable multi-threaded parallelism in Julia
Remark.jl created by Pietro Vertechi
"Create markdown presentations from Julia"
JuDoc.jl created by Thibaut Lienart
"Static site generator. Simple, fast, compatible with basic LaTeX, maths with KaTeX, optional pre-rendering, written in Julia."
Pluto.jl created by Fons Varder Plas
"Reactive Notebook, written in Julia."
Plots.jl created by Tom Breloff
"Plotting metapackage: it's an interface over many different plotting libraries."

Useful links

A Comprehensive Tutorial to Learn Data Science with Julia from Scratch

10 Reasons Why You Should Learn Julia

Noteworthy Differences from other Languages

Julia Cheat Sheet