Julia Computing

In the course Julia Computing from SpiralTrain the participants learn to program with the dynamic programming language Julia, which is widely used in scientific calculations and gives a very good perf

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.

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.

Modules

Module 1 : Julia Intro

• Intro Julian World
• JIT Compiler
• Installing Julia
• JuliaBox
• Package Installation
• Role in Data Science
• Julia Features
• Parallel Processing
• Multiple Dispatch
• Homoiconic Macros
• Interlanguage Cooperation

Module 2 : Julia Language

• Variables
• Data Types
• Number Systems
• Classes and Objects
• Object References
• Floating Points
• Flow Control
• Operators
• Strings
• String Interpolation
• Common String Functions

Module 3 : Data Structures

• Arrays and Indexing
• Multiple Dimensions
• Generator Expressions
• Sorting
• Ellipsis Operator
• Sets
• Dictionaries
• Keys and Values
• Matrices
• Matrix Multiplication
• Broadcasting

Module 4 : Functions

• Defining Functions
• Parameter Passing
• Multiple Inputs
• Variable Argument Lists
• Multiple Outputs
• Anonymous Functions
• Map and Reduce
• Multiple Dispatches
• Operators as Functions
• Returning Functions

Module 5 : Working with Data

• Stream and Text I/O
• Byte Array Streaming
• Reading Files
• Structured Data Sets
• CSV and DLM Files
• DataFrames
• RDataSets
• Statistics and Estimations
• Pandas
• Time Series

Module 6 : Plotting

• Data Visualization
• Plot as Object
• Plots Package
• Default Plot Behavior
• Decorating Plots
• SubPlots
• Graphic Engines
• Plotly
• Bokeh
• Images

Module 7 : Databases

• Database Interface
• ODBC and JDBC
• SQLite
• NoSQL Datastores
• Key Value Systems
• Document Datastores
• RESTful interfacing
• HTTP Verbs
• JSON and XML

Module 8 : Interoperability

• Calling C and Fortran
• Julia API
• Calling API from C
• Metaprogramming
• Symbols
• Macros
• Error Handling
• Redirection and Pipes
• Parallel Operations

Module 9 : Working with Julia

• Networking
• Frequency Analysis
• Stochastic Simulations
• Bayesian Methods
• Optimization Problems
• JuliaWeb Group
• Cloud Services
• AWS Cloud
• Google Cloud