Name: Julia Training
Item: Julia Training
Rating: 5
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Julia Training

Julia is a rapidly emerging programming language with a strong focus on numerical accuracy, scientific computing and statistics. It has gained most of its reputation due to its speed of execution in conjunction with its ease of programming. Julia has a wealth of built-in and external tools for distributed and parallel computing. It facilitates the construction of user-defined data structures, and makes it easy to do meta programming, therefore it also define your own DSLs and it allows interacting with several other programming languages such as C, Python and R. Julia provides a multiple-dispatch programming paradigm, which in many ways helps you organize your code and makes you a better programmer and software engineer.

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

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