• Trity Course Scilab IoT

    Scilab for the Internet of Things

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  • Trity Course RPi IoT

    Raspberry Pi for the Internet of Things (with Pi-3)

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  • Trity Course Scilab AI

    Artificial Intelligence with Scilab

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  • Trity Course Scilab NCV

    Numerical Computation and Visualization with Scilab

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  • Trity Course Scilab IP

    Scilab for Image Processing and Computer Vision

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  • Trity Course Scilab BDA

    Big Data Training Series : Practical Guide to Big Data Analytics with Pig Latin, Hive and Scilab

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  • Trity Course Scilab DM

    Scilab for Data Mining

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  • Python Deep Learning

    Python for Machine and Deep Learning

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Scilab Courses

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Scilab is an open source, cross-platform numerical computational package and a high-level, numerically oriented programming language. It can be used for signal and image processing, statistical analysis, Internet of Things, data mining, etc. In Trity Technologies we have developed more than 20 courses based on Scilab since last few years.

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Raspberry Pi Courses

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The Raspberry Pi is a series of credit card–sized single-board computers developed in the United Kingdom by the Raspberry Pi Foundation with the intent to promote the teaching of basic computer science in schools and developing countries. Our very first Raspberry Pi Training is the aplication in IoT, and we are extending the training into other fields from time to time. 

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E4Coder - Automatic Code Generation

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E4Coder is a set of tools that can be used to simulate control algorithms and to generate code for embedded microcontrollers running with or without a realtime operating system. Our course focus on using the block diagram for algorithms development and the codes would be automatically generated and downloaded into the embedded boards such as Arduino Uno. A mobile robot application would be used for the training for practical hands-on. 

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Scilab for Data Mining
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Process, Analyse and Visualize Data with Scilab

Scilab provides tools for data mining purposes, from the connections to the databases to the analysis tools and visualisation tools. 

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“With Scilab, data is under my control...”
Course Synopsis

dataminimage grapData mining, or knowledge discovery, is the computer-assisted process of digging through and analysing enormous sets of data and then extracting the meaning of the data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, knowledge-driven decisions.

Data mining techniques are used in a many research areas, including mathematics, cybernetics, genetics and marketing.

The phrase data mining is commonly misused to describe software that presents data in new ways. True data mining software doesn't just change the presentation, but actually discovers previously unknown relationships among the data.

Given information databases of sufficient size and quality, data mining technology can generate new business opportunities by providing these capabilities:

-Data mining automates the process of finding predictive information in large databases. Questions that traditionally required extensive hands-on analysis can now be answered quickly.

-Data mining sweeps through databases and identify previously hidden patterns in one step.


Course Objectivesdataminimage excelc

This course is intended as a practical hands-on experience to data mining with Scilab for the use in engineering and sciences. As an open source software, Scilab provides a powerful computing environment for your analysis. A series of hands-on exercises will help you to understand better and more efficiently in the topics outlined here

Who Must Attend

Engineers, researchers, scientists from academic, science and engineering or simply anyone who wants to work and retrieve valuable information from the available data.


Basic knowledge of computer operation and data analysis are preferred. We will bring you to speed in understanding in using the Scilab program

Course Outline
Understanding Scilab
  • Overview of Scilab
This session will provide basic understanding of Scilab
and how it could be used for data mining.
Importing and Exporting Data
  • Importing data from Scilab Workspace
  • Importing data from files (txt, xls, csv, etc)
  • Importing data from web interface
  • Importing data from database
Learn how to import and exporting data from and to
various sources, such as into files in different formats,
database, or even to the cloud.
Processing Data
  • Dealing with Outliers
  • Dealing with NaNs and Infs
  • Removing duplicates data
  • Filtering data
Processing data is an important stage before jumping 
into data visualization or modelling. Irrelevant data
could cause wrong model which yield to the wrong interpretation of the data.
Visualizing Data
  • Line Plot
  • Bar Chart and Pie Chart
  • Histograms
  • Matrix Plot
  • 3D Visualisation
Different methods of Visualizing data help us to have better understanding of data. This will give us a big picture of what we are going to do with the data before jumping into it in "blind" and end up with "rubbish in, rubbish out".
Conventional Analysis
  • Statistical Analysis
  • Correlation and Linear Regression
  • Data Fitting

Understanding basic concept of statistical analysis of data, study the meaningful relations of the data and representing data with equation.


Artificial Intelligence
  • Classification and Clustering techniques
  • Fuzzy Logic Example (FLS)
  • Neural Network Example (NN)
  • Support Vector Machine Example (SVM)
Exploring more advance methods in analysing the data, including the artificial intelligence methods which as Fuzzy, Neural and SVM.
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