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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.
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.
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.
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.
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.
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.
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.
The course Julia Computing is targeted at Big Data analysts and scientists who want to use Julia to analyze data and make static analyses.
Experience with programming is beneficial to good understanding but is not required.
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.
After successful completion of the course, participants receive an official certificate Julia Computing.
Module 1 : Julia Intro
Module 2 : Julia Language
Module 3 : Data Structures
Intro Julian World
Role in Data Science
Classes and Objects
Common String Functions
Arrays and Indexing
Keys and Values
Module 4 : Functions
Module 5 : Working with Data
Module 6 : Plotting
Variable Argument Lists
Map and Reduce
Operators as Functions
Stream and Text I/O
Byte Array Streaming
Structured Data Sets
CSV and DLM Files
Statistics and Estimations
Plot as Object
Default Plot Behavior
Module 7 : Databases
Module 8 : Interoperability
Module 9 : Working with Julia
ODBC and JDBC
Key Value Systems
JSON and XML
Calling C and Fortran
Calling API from C
Redirection and Pipes
All our courses are classroom courses in which the students are guided through the material on the basis of an experienced trainer with in-depth material knowledge. Theory is always interspersed with exercises.
We also do custom classes and then adjust the course content to your wishes. On request we will also discuss your practical cases.
The course times are from 9.30 to 16.30. But we are flexible in this. Sometimes people have to bring children to the daycare and other times are more convenient for them. In good consultation we can then agree on different course times.
We take care of the computers on which the course can be held. The software required for the course has already been installed on these computers. You do not have to bring a laptop to participate in the course. If you prefer to work on your own laptop, you can take it with you if you wish. The required software is then installed at the start of the course.
Our courses are generally given with Open Source software such as Eclipse, IntelliJ, Tomcat, Pycharm, Anaconda and Netbeans. You will receive the digital course material to take home after the course.
The course includes lunch that we use in a restaurant within walking distance of the course room.
The courses are planned at various places in the country. A course takes place at a location if at least 3 people register for that location. If there are registrations for different locations, the course will take place at our main location, Houten which is just below Utrecht. A course at our main location also takes place with 2 registrations and regularly with 1 registration. And we also do courses at the customer’s location if they appreciate that.
At the end of each course, participants are requested to evaluate the course in terms of course content, course material, trainer and location. The evaluation form can be found at https://www.klantenvertellen.nl/reviews/1039545/spiraltrain?lang=en. The evaluations of previous participants and previous courses can also be found there.
The intellectual property rights of the published course content, also referred to as an information sheet, belong to SpiralTrain. It is not allowed to publish the course information, the information sheet, in written or digital form without the explicit permission of SpiralTrain. The course content is to be understood as the description of the course content in sentences as well as the division of the course into modules and topics in the modules.