Lilly Tech Systems
Julia for Data Science
Harness Julia's speed and expressiveness for data science — from DataFrames and visualization to deep learning with Flux.jl and machine learning with MLJ.jl.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is Julia, why it was created, the two-language problem, and Julia's advantages for data science.
2. Setup
Install Julia, configure the REPL, manage packages with Pkg, set up VS Code, and Jupyter integration.
3. DataFrames
DataFrames.jl for tabular data: loading CSV, filtering, grouping, joining, and transforming data.
4. Visualization
Create plots with Plots.jl and Makie.jl: scatter, line, bar, heatmaps, and interactive visualizations.
5. ML with Flux
Deep learning with Flux.jl: building neural networks, training loops, GPU acceleration, and MLJ.jl.
6. Best Practices
Performance tips, type stability, benchmarking, project structure, and Julia vs Python comparison.
What You'll Learn
By the end of this course, you'll be able to:
Write Fast Julia Code
Leverage Julia's JIT compilation to write code as fast as C but as readable as Python.
Analyze Data
Load, clean, transform, and analyze datasets using DataFrames.jl and the Split-Apply-Combine paradigm.
Visualize Results
Create publication-quality plots and interactive visualizations with Plots.jl and Makie.jl.
Build ML Models
Train deep learning models with Flux.jl and use MLJ.jl for traditional machine learning pipelines.