Introduction:

Julia Training briefs 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.

Course Content:

1.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

2.Strings: Hello World

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

3.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

4.Arrays

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

5.Other Elementary Types

• Tuples
• Ranges
• Dictionaries
• Symbols

6.Building Your Own Types

• Abstract types
• Composite types
• Parametric composite types

7.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

8.Constructors

• Inner constructors
• Outer constructors

9.Control Flow

• Compound expressions and scoping
• Conditional evaluation
• Loops
• Exception Handling
• Tasks

10.Code Organization

• Modules
• Packages

11.Metaprogramming

• Symbols
• Expressions
• Quoting
• Internal representation
• Parsing
• Evaluation
• Interpolation

12.Reading And Writing Data

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

13.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

14.Plotting

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

15.Parallel Computing

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