Julia Training

Julia Training discusses the basics of the Julia programming language with a strong focus on numerical accuracy, scientific computing and statistics. Julia programs are organized around the multiple dispatch; by defining functions and overloading them for different argument types, which can also be user-defined.

Julia Training Curriculum

Introduction to Julia

What niche is filled by Julia
How can Julia help you with data analysis
Getting started with Julia’s REPL
Alternative environments for Julia development: Juno, IJulia and Sublime-IJulia
The Julia ecosystem: documentation and package search
Getting more help: Julia forums and Julia community

Strings: Hello World

Introduction to Julia REPL and batch execution via “Hello World”
Julia String Types

Scalar Types

What is a variable? Why do we use a name and a type for it?
Integers
Floating point numbers
Complex numbers
Rational numbers

Arrays

Vectors
Matrices
Multi-dimensional arrays
Heterogeneous arrays (cell arrays)
Comprehensions

Other Elementary Types

Tuples
Ranges
Dictionaries
Symbols

Building Your Own Types

Abstract types
Composite types
Parametric composite types

Functions

How to define a function in Julia
Julia functions as methods operating on types
Multiple dispatch
How multiple dispatch differs from traditional object-oriented programming
Parametric functions
Functions changing their input
Anonymous functions
Optional function arguments
Required function arguments

Constructors

Inner constructors
Outer constructors

Control Flow

Compound expressions and scoping
Conditional evaluation
Loops
Exception Handling
Tasks

Code Organization

Modules
Packages

Metaprogramming

Symbols
Expressions
Quoting
Internal representation
Parsing
Evaluation
Interpolation

Reading and Writing Data

Filesystem
Data I/O
Lower Level Data I/O
Dataframes

Distributions and Statistics

Defining distributions
Interface for evaluating and sampling from distributions
Mean, variance and co variance
Hypothesis testing
Generalized linear models: a linear regression example

Plotting

Plotting packages: Gadfly, Winston, Gaston, PyPlot, Plotly, Vega
Introduction to Gadfly
Interact and Gadfly

Parallel Computing

Introduction to Julia’s message passing implementation
Remote calling and fetching
Parallel map (pmap)
Parallel for
Scheduling via tasks
Distributed arrays.