Basic MATLAB Programming Training Course

This four-day course establishes a solid foundation in image processing using Matlab. You will gain hands-on experience in modifying and enhancing images, as well as extracting meaningful patterns from them. Additionally, you will learn how to construct and apply 2D filters to images.

Throughout the analysis process, practical examples and exercises illustrate the effective use of relevant Matlab and Image Processing Toolbox functionality.

This three-day course offers a comprehensive introduction to the MATLAB technical computing environment. It is designed for beginners and those seeking a refresher. No prior programming experience or knowledge of MATLAB is assumed. Throughout the course, key themes such as data analysis, visualisation, modelling, and programming are explored. Topics include:

• Working with the MATLAB user interface
• Entering commands and creating variables
• Analysing vectors and matrices
• Visualising vector and matrix data
• Working with data files
• Working with data types
• Automating commands with scripts
• Writing programs with logic and flow control
• Writing functions

During this instructor-led, live training session, participants will learn how to utilise Matlab to design, construct, and visualise a convolutional neural network for image recognition.

Upon completion of this training, participants will be able to:

• Construct a deep learning model
• Automate the data labelling process
• Utilise models from Caffe and TensorFlow-Keras
• Train data using multiple GPUs, the cloud, or clusters

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

In the first part of this training, we cover the fundamentals of MATLAB and its role as both a programming language and a development platform. This discussion includes an introduction to MATLAB syntax, arrays and matrices, data visualisation, script development, and object-oriented principles.

In the second part, we demonstrate how to use MATLAB for data mining, machine learning, and predictive analytics. To give participants a clear and practical understanding of MATLAB's approach and capabilities, we draw comparisons between using MATLAB and other tools such as spreadsheets, C, C++, and Visual Basic.

In the third part of the training, participants learn how to streamline their workflows by automating data processing and report generation.

Throughout the course, participants will apply the concepts they have learned through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough understanding of MATLAB's capabilities and will be able to use it to solve real-world data science problems, as well as to streamline their work through automation.

Assessments will be conducted throughout the course to gauge progress.

Course Format

• The course includes both theoretical and practical exercises, including case discussions, sample code inspection, and hands-on implementation.

Note

• Practice sessions will be based on pre-arranged sample data report templates. If you have specific requirements, please contact us to arrange them.

This instructor-led, live training in New Zealand (available online or on-site) is designed for beginner-level developers or engineers who wish to learn how to apply numerical simulation techniques to dynamic problems using Matlab.

By the conclusion of this training, participants will be able to:

• Grasp the fundamentals of dynamic analysis.
• Utilise Matlab to carry out analytical and numerical solutions.
• Derive equations of motion using various approaches.

MATLAB seamlessly integrates computation, visualisation, and programming within an intuitive environment. It provides a Financial Toolbox equipped with the necessary features to conduct mathematical and statistical analysis of financial data, alongside the capability to present findings through high-quality graphics.

This instructor-led training offers an introduction to using MATLAB for finance. Participants will explore data analysis, visualisation, modelling, and programming through hands-on exercises and extensive in-lab practice.

By the conclusion of this training, participants will possess a comprehensive understanding of the powerful features within MATLAB's Financial Toolbox and will have gained sufficient practical experience to apply these tools immediately to solve real-world problems.

Audience

• Financial professionals with prior experience using MATLAB

Course format

• A blend of lectures, discussions, and intensive hands-on practice

This course offers a comprehensive introduction to the MATLAB technical computing environment, alongside an introduction to applying MATLAB for financial applications. Designed for beginners and those seeking a refresher, no prior programming experience or knowledge of MATLAB is required. Throughout the course, key themes such as data analysis, visualisation, modelling, and programming are explored. Topics include:

• Working with the MATLAB user interface
• Entering commands and creating variables
• Analysing vectors and matrices
• Visualising vector and matrix data
• Working with data files
• Working with data types
• Automating commands with scripts
• Writing programs with logic and flow control
• Writing functions
• Using the Financial Toolbox for quantitative analysis

Object-Oriented Programming with MATLAB for Geophysics involves applying MATLAB's OOP features to develop structured, reusable, and maintainable code tailored for geophysical data analysis and modelling.

This instructor-led, live training (available online or on-site) is designed for beginner-level MATLAB users in geophysics who wish to learn how to design and implement object-oriented solutions for geophysical applications.

By the end of this training, participants will be able to:

• Grasp the fundamentals of object-oriented programming in MATLAB.
• Create and work with classes, properties, and methods for handling geophysical data.
• Apply OOP techniques to structure geophysical modelling and analysis workflows.
• Enhance code reusability, scalability, and clarity for geoscience projects.

Course Format

• Interactive lectures and discussions.
• Abundant exercises and hands-on practice.
• Live implementation in a lab environment.

Course Customisation Options

• To request a customised training session for this course, please contact us to arrange.

MATLAB is a numerical computing environment and programming language developed by MathWorks.

Predictive analytics is the process of leveraging data analytics to forecast future outcomes. This approach combines data with data mining, statistical methods, and machine learning techniques to develop predictive models that anticipate future events.

In this instructor-led, live training, participants will learn how to use Matlab to construct predictive models and apply them to large sample data sets, enabling predictions about future events based on historical data.

By the end of this training, participants will be able to:

• Develop predictive models to analyse patterns in historical and transactional data
• Apply predictive modelling to identify risks and uncover opportunities
• Build mathematical models that capture key trends
• Leverage data from devices and business systems to reduce waste, save time, or cut costs

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• A blend of lecture, discussion, exercises, and extensive hands-on practice

This two-day course offers a comprehensive introduction to the MATLAB® technical computing environment. It is designed for beginners as well as those seeking a refresher. No prior programming experience or familiarity with MATLAB is required. Throughout the course, key themes such as data analysis, visualisation, modelling, and programming are explored in depth.

This course is designed to introduce the fundamentals of the Systems Modeling Language (SysML), its application using MagicDraw/Cameo software, foundational Model-Based Systems Engineering (MBSE) simulation techniques, and industry best practices in MBSE. It also equips professionals with essential background knowledge in architectural simulation, an overview of the Simulation Toolkit plugin, methods for simulating various diagram types, and strategies for linking diagram simulations to automate architectural processes.​

This course is designed for anyone seeking a powerful alternative to the commercial MATLAB package. Over three days, participants gain comprehensive knowledge of navigating the Octave environment and leveraging it for data analysis and engineering calculations. The training caters to both beginners and those already familiar with the software who wish to systematise their knowledge and refine their skills. While prior experience with other programming languages is not required, it will significantly accelerate the learning process. Throughout the course, you will explore numerous practical examples demonstrating how to effectively utilise Octave in real-world scenarios.

This instructor-led, live training in New Zealand (delivered either online or on-site) is designed for Matlab users who wish to explore and/or transition to Python for data analytics and visualisation.

By the end of this training, participants will be able to:

• Install and configure a Python development environment.
• Understand the key differences and similarities between Matlab and Python syntax.
• Use Python to extract insights from a variety of datasets.
• Migrate existing Matlab applications to Python.
• Integrate Matlab and Python applications seamlessly.

Simulink is a graphical programming environment for modelling, simulating, and analysing multidomain dynamic systems.