pszufe/2024_julia_ec_economicdep

Julia bootcamp course

Dates: Feb 20-21, 2024

Time: 09:00-17:00 (includes breaks)

Location: Directorate General for Economic and Financial Affairs – Unit B2 Rue de la Loi 170/Wetstraat 170 1049 Bruxelles/Brussel Belgium

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 (all times are EST time zone)

Day 1 (Tuesday, Feb 20, 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 Sources Simple Delimited Files CSV JSON Microsoft Excel Apache Arrow
	3 Data Visualizations with Plots.jl Working with Plots.jl and backends Animations Plots for scientific reports
	4 Julia Performance condiderations Code benchmarking Basic performance considerations
Day 2 (Wednesday, Feb 21, 2024)	5 Data Transformations and Analysis Introduction to Data Frames, data transformations Transforming and processing tabular data for modelling Statistical analysis and predictive modeling
	6_Scaling_out_Computations Single Instruction Multiple Data (SIMD) Green threading Multi-threading Parallel and distributed computing