Julia Online Training

Julia Online Training



    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


    • 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


    • 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


    • Inner constructors
    • Outer constructors

    9.Control Flow

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

    10.Code Organization

    • Modules
    • Packages


    • 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


    • 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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    The Training itself is Real-time Project Oriented.

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