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Welcome to the Julia workshop for Data Science

This repository contains the material for the "Julia workshop for Data Science" taught at the ISMB 2022 conference on July 10th, 2022.

  • Welcome to the Julia workshop for Data Science!
  • The goal for the workshop is to highlight the main features that make Julia an attractive option for data science programmers
  • The workshop is intended for any data scientist with experience in R and/or python who is interested in learning the attractive features of Julia for Data Science. No knowledge of Julia is required.
  • Workshop materials in the github repository julia-workshop

Learning Objectives for Tutorial

At the end of the tutorial, participants will be able to:

  • Identify the main features that make Julia an attractive language for Data Science
  • Set up a Julia environment to run their data analysis
  • Efficiently handle datasets (even across different languages) through Tables.jl and Arrow.jl
  • Fit (generalized) linear mixed models with MixedModels.jl
  • Communicate across languages (Julia, R, python)

Intended audience and level: The tutorial is intended for any data scientist with experience in R and/or python who is interested in learning the attractive features of Julia for Data Science. No knowledge of Julia is required.

Schedule

Time Topic Presenter
11:00 - 11:30 Session 1: Get Started with Julia Claudia Solis-Lemus
11:30 - 12:30 Session 2a: Data Tables and Arrow files Douglas Bates
12:30 - 1:00 Session 2b: Interval Overlap Douglas Bates
1:00 - 2:00 Lunch break
2:00 - 3:00 Session 3: Model fitting
3:00 - 4:00 Session 4: Hands-on exercise Sam Ozminkowski and Bella Wu
4:00 - 4:15 Coffee break
4:15 - 5:00 Presentation of selected participants' scripts and Q&A
5:00 - 5:30 Session 5: Other important Data Science tools Claudia Solis-Lemus
5:30 - 6:00 Session 6: Conclusions and questions Claudia Solis-Lemus

In preparation for the workshop

Participants are required to follow the next steps before the day of the workshop:

  1. Git clone the workshop repository: git clone https://github.com/crsl4/julia-workshop.git

  2. Install Julia. The recommended option is to use JuliaUp:

  • Windows: winget install julia -s msstore

  • Mac and Linux: curl -fsSL https://install.julialang.org | sh

  • Homebrew users: brew install juliaup

After JuliaUp is installed, you can install different Julia versions with:

juliaup add release  ## installs release version
juliaup add rc     ## installs rc version
juliaup st           ## status of julia versions installed
juliaup default rc ## making beta version the default
  1. Choose a dataset along with a script to analyze it written in another language (R or python) as we will spend part of the workshop translating participants' scripts to Julia.

Want to learn more?

Checkout the great resources in Julia learning.

Previous versions of workshops

  • This "WID Julia workshop" release contains the notes of the Julia workshop taught at the WID Data Science Research Bazaar on February 10th, 2021.
  • The "CIMAT workshop" release contains the notes of the Julia workshop taught at CIMAT in October 26-27, 2020.