pszufe / 2024_julia_aberdeen

Julia bootcamp course

Dates: Feb 26-28, 2024, Hours: 10:00-12:00, 13:00-14:30 + open Q&A

Location: James Hutton Institute, Aberdeen, Scotland

Data analysis has become one of the core processes in virtually any professional activity. The collection of data becomes easier and less expensive, so we have ample access to it.

The Julia language, which was designed to address the typical challenges that data scientists face when using other tools. Julia is like Python, in that it supports an efficient and convenient development process. At the same time, programs developed in Julia have performance comparable to C.

During this 2 day boot camp, you will learn how to build data science models using Julia. Moreover, we will teach you how to scale your computations beyond a single computer.

This course does not require the participants to have prior detailed knowledge of advanced machine learning algorithms nor the Julia programming language. What we assume is a basic knowledge of data science tools (like Python or R) and techniques (like linear regression, basic statistics, plotting).

Schedule

Day 1 (Monday, Feb 26, 2024)	1 Basics What is the Julia language - motivation and key design concepts, managing virtual environment and packages Installing and running Julia, Julia IDE (VS Code, Jupyter notebook) Getting help in Julia and available resources about Julia Basic data structures (dictionaries, tuples, matrices, structures)
	2 Working with Data Simple Delimited Files, CSV, JSON, Microsoft Excel, Apache Arrow Analytic workflow with data frames
Day 2 (Tuesday, Feb 27, 2024)	3 Running simulation models in Julia Working with Plots.jl and animations Sample simulation models
	4 Optimization modelling with JuMP.jl Optimization models Spatial data and optimization models
Day 3 (Wednesday, Feb 28, 2024)	5_Scaling_out_Computations Single Instruction Multiple Data (SIMD) Green threading Multi-threading Parallel and distributed computing
	6 Hacking Julia Performance Code benchmarking Basic performance considerations