Course Julia Computing

In the course Julia Computing the participants learn to program with the dynamic programming language Julia, which is widely used in scientific calculations and gives a very good performance. Like Python and R, Julia is used for statistical calculations and data analysis, but the execution speed of Julia is much better compared to Python and R. Julia is ideally suited for big data analysis and supports complex tasks such as cloud computing and parallel execution.

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  • Course Julia Computing : Content

    Julia Features

    The course Julia Computing starts with an overview of Julia's JIT compiler and package installation and how Julia can also be run online with JuliaBox in combination with Jupyter notebooks. Also discussed are the main features of Julia such as Parallel Processing, Multiple Dispatch and Homoiconic Macros.

    Julia Syntax

    Then the Julia language is treated with variables, data types, operators, classes and objects and control flow structures. Composite data structures such as arrays, sets, dictionaries and matrices and operations on them such as generator expressions and broadcasting are also discussed.

    Functions in Julia

    Also part of the program of the course Julia Computing are functions in Julia. Functions with multiple inputs and outputs and variable argument lists are treated and as well as anonymous functions and higher order functions such as map and reduce.

    Plotting with Julia

    Naturally attention is also paid in the course Julia Computing to reading, processing and plotting data in Julia. Reading CSV and DLM files into DataFrames and making statistical calculations with the panda's library is covered. Data visualization with plot libraries such as Plotly and Bokeh is also treated.

    Julia and Data

    Then it is time to discuss how SQL and NoSQL databases can be accessed in Julia and how REST Services can be used to read JSON and XML data.

    Julia's Interoperability

    Finally the interoperability of Julia with other languages ​​such as Fortran and C is on the schedule of the course Julia Computing and a number of advanced applications of Julia such as Cloud computing are discussed.

  • Course Julia Computing : Training

    Audience Course Julia Computing

    The course Julia Computing is targeted at Big Data analysts and scientists who want to use Julia to analyze data and make static analyses.

    Prerequisites Course Julia Computing

    Experience with programming is beneficial to good understanding but is not required.

    Realization Training Julia Computing

    The theory is discussed on the basis of presentations and examples. The concepts are explained with demos. There is ample time to practice the theory yourself. Juno is used as a development environment. Course times are from 9:30 am to 16:30 pm.

    Certification Course Julia Computing

    After successful completion of the course, participants receive an official certificate Julia Computing.

    Course Julia Computing
  • Course Julia Computing : Modules

    Module 1 : Julia Intro

    Module 2 : Julia Language

    Module 3 : Data Structures

    Intro Julian World
    JIT Compiler
    Installing Julia
    Package Installation
    Role in Data Science
    Julia Features
    Parallel Processing
    Multiple Dispatch
    Homoiconic Macros
    Interlanguage Cooperation
    Data Types
    Number Systems
    Classes and Objects
    Object References
    Floating Points
    Flow Control
    String Interpolation
    Common String Functions
    Arrays and Indexing
    Multiple Dimensions
    Generator Expressions
    Ellipsis Operator
    Keys and Values
    Matrix Multiplication

    Module 4 : Functions

    Module 5 : Working with Data

    Module 6 : Plotting

    Defining Functions
    Parameter Passing
    Multiple Inputs
    Variable Argument Lists
    Multiple Outputs
    Anonymous Functions
    Map and Reduce
    Multiple Dispatches
    Operators as Functions
    Returning Functions
    Stream and Text I/O
    Byte Array Streaming
    Reading Files
    Structured Data Sets
    CSV and DLM Files
    Statistics and Estimations
    Time Series
    Data Visualization
    Plot as Object
    Plots Package
    Default Plot Behavior
    Decorating Plots
    Graphic Engines

    Module 7 : Databases

    Module 8 : Interoperability

    Module 9 : Working with Julia

    Database Interface
    ODBC and JDBC
    NoSQL Datastores
    Key Value Systems
    Document Datastores
    RESTful interfacing
    HTTP Verbs
    JSON and XML
    Calling C and Fortran
    Julia API
    Calling API from C
    Error Handling
    Redirection and Pipes
    Parallel Operations
    Frequency Analysis
    Stochastic Simulations
    Bayesian Methods
    Optimization Problems
    JuliaWeb Group
    Cloud Services
    AWS Cloud
    Google Cloud
  • Course Julia Computing : General

    Read general course information
  • Course Julia Computing : Reviews

  • Course Julia Computing : Certificate