JULIA Online Training

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JULIA Online Training


 (4.9) | 1500 Ratings


Introduction


JULIA Online Training Details
Track Regular Track Weekend Track Fast Track
Course Duration 30 Hrs 8 Weekends 5 Days
Hours 1hr/day 2 Hours a day 6 Hours a day
Training Mode Online Classroom Online Classroom Online Classroom
Delivery Instructor Led-Live Instructor Led-Live Instructor Led-Live


Course 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

Exam & Certification

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Course 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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