Presentation on Data Science with Julia
Based on presentations by John Myles White, Stefan Karpinski and others
This talk goes through brief introductions to the Julia language and some of the packages available for data wrangling, visualization, analysis and prediction.
- Background
- Why use Julia?
- Language Basics
- Types
- Linear Algebra
- Functions, Multiple Dispatch
- Programming Styles
- Package Manager
- Statistics in Julia
- Tabular Data
- Data Visualization
- Machine Learning Algorithms
- Unsupervised
- Supervised
- Resources
The IJulia Notebook can be viewed here.
Run setup.jl to install necessary packages. You'll need IPython Notebook v1.0 or later installed to run the IJulia Notebook.