Julia for Statistics and Data Science

• Activity Number: 30 - Introductory Overview Lecture: Julia for Statistics and Data Science
• Type: Invited
• Date/Time: Sunday, August 8, 2021 : 3:30 PM to 5:20 PM
• Sponsor: JSM Partner Societies
• Author(s): Douglas Bates* and Cecile Ane and Claudia Solis-Lemus
• Companies: University of Wisconsin and University of Wisconsin and University of Wisconsin

Abstract

Julia has been called the programming language of the 21st century for scientific computing, data science, and machine learning. As a high-level, high-performance, dynamic language, Julia is faster than other scripting languages because of smart design decisions like type-stability through specialization via multiple-dispatch. Julia's code can be efficient and concise, which leads to clear performance gains. In addition, Julia's environments are fully reproducible, and it is easy to express object-oriented and functional programming patterns.

This workshop will provide an introduction to key Data Science tools in Julia such as reproducible project management with DrWatson.jl, data management with Arrow.jl and Tables.jl and (Generalized) linear mixed models with MixedModels.jl. Unlike widely used R packages, all packages that we will describe are written 100% in Julia thus illustrating the language’s potential to overcome the two-language problem.

The target audience is anyone at JSM who wants to learn more about Julia and some of the existing Julia packages that are already available for Statistics and Data Science.