- Machine Learning Workshop (Matt Bauman)
- Writing a package – a thorough guide (Kristoffer Carlsson, Frederik Ekre)
- Excelling at Julia Basics and Beyond (Huda Nassar, Jane Herriman)
- Parallel Computing (Matt Bauman, Avik Sengupta)
- Debugging Code with JuliaInterpreter presented by Kristoffer Carlsson, Tim Holy and Sebastian Pfitzner
Presentation of a Julia debugger and demonstration of a variety of interfaces for accessing it. - LightQuery.jl presented by Alex Lew
dplyr-like in Julia - DataKnots.jl presented by Clark C. Evans
"Extensible, practical and coherent algebra of query combinators" - Literate.jl developped by Frederik Ekre
"The main purpose is to facilitate writing Julia examples/tutorials that can be included in your package documentation. Literate can generate markdown pages (for e.g. Documenter.jl), and Jupyter notebooks, from the same source file." - Generating Documentation: Under the Hood of Documenter.jl presented by Morten Piibeleth
"A documentation generator for Julia." - Queryverse.jl presented by David Anthoff
"A meta package for data science in Julia." - How We Wrote a Textbook using Julia presented by Tim Wheeler
"A template for textbooks in the same style as Algorithms for Optimization." - MendelIHT.jl: Generalized Linear Models for High Dimensional Genetics (GWAS) Data presented by Benjamin Chu
"Sparse GLM regression for High Dimensional Data."
- Annual Julia User & Developer Survey 2019 presented by Viral Shah.
- Announcing composable multi-threaded parallelism in Julia
- Remark.jl developped by Pietro Vertechi
"Create markdown presentations from Julia" - JuDoc.jl developped by Thibaut Lienart
"Static site generator. Simple, fast, compatible with basic LaTeX, maths with KaTeX, optional pre-rendering, written in Julia."
A Comprehensive Tutorial to Learn Data Science with Julia from Scratch
10 Reasons Why You Should Learn Julia