Julia Online Training

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

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