Programming Using MATLAB

About This Course: This course provides an introduction to MATLAB and it is designed to give trainees fluency in MATLAB. The course consists of interactive lectures with trainees doing example MATLAB problems during the lab sessions.


Training Cost: ₦ 55,000.00


Background on MATLAB:  MATLAB is a programming language developed by MathWorks. MATLAB is intended primarily for numerical computing and, as such, MATLAB users come from various backgrounds of engineering, science, and economics. Indeed, the richness of the MATLAB computational environment combined with an integrated development environment (IDE) and a straightforward interface and toolboxes, and its simulation and modelling capabilities mean that it has grown to become a de facto development language for prototyping and deployable applications, and an indispensable tool for scientists, researchers, and engineers. 

Simulink® is a block diagram environment for multidomain simulation and Model-Based Design. It is integrated with MATLAB®, enabling you to incorporate MATLAB algorithms into models and export simulation results to MATLAB for further analysis. It supports simulation, automatic code generation, and continuous test and verification of embedded systems. Simulink provides a graphical editor, customizable block libraries, and solvers for modeling and simulating dynamic systems.


Seven Reasons MATLAB Is the Easiest and Most Productive Environment for Engineers and Scientists:

1. MATLAB Speaks Math: Engineers and scientists need a programming language that lets them express matrix and array mathematics directly. Linear algebra in MATLAB looks like linear algebra in a textbook. The same is true for data analytics, signal and image processing, control design, and other applications. This is why more than 1,500 textbooks teach using MATLAB.


2. MATLAB Is Designed for Engineers and Scientists: Everything about MATLAB is designed specifically for engineers and scientists:

  • Function names and signatures are familiar and memorable.
  • The desktop environment is tuned for iterative engineering and scientific workflows.
  • Documentation is written for engineers and scientists, not computer scientists.


3. MATLAB Toolboxes Just Work: MATLAB toolboxes offer professionally developed, rigorously tested, field-hardened, and fully documented functionality for a wide range of scientific and engineering applications. Toolboxes are designed to work together, and they integrate with parallel computing environments, GPUs, and C code generation.


4. MATLAB Has Apps: MATLAB apps are interactive applications that combine direct access to large collections of algorithms with immediate visual feedback. You can instantly see how different algorithms work with your data. Iterate until you’ve got the results you want, then automatically generate a MATLAB program to reproduce or automate your work.


5. MATLAB Integrates Workflows: Major engineering and scientific challenges require broad coordination across teams to take ideas to implementation. Every handoff along the way adds errors and delays. MATLAB helps automate the entire path from research to production.


6. MATLAB Is Fast: MATLAB does the hard work of making your code fast. Math operations are distributed across your computer’s cores, library calls are heavily optimized, and all code is just-in-time compiled. You can run your algorithms in parallel by simply changing for-loops into parallel for-loops or by changing standard arrays into GPU arrays. Run parallel algorithms in an infinitely scalable cloud with no code changes.


7. MATLAB Is Trusted: Engineers and scientists trust MATLAB to send a spacecraft to Pluto, match transplant patients with organ donors, or just compile a report for management. This trust is built on impeccable umeric stemming from the strong roots of MATLAB in the numerical analysis research community. A team of MathWorks engineers continuously verifies quality by running millions of tests on the MATLAB code base every day.


What you can do with MATLAB and Simulink:

1. Deep Learning: With just a few lines of MATLAB® code, you can build deep learning models without having to be an expert. You can use MATLAB to perform tasks such as targeting embedded GPUs, using a long short-term memory (LSTM) network, or performing semantic segmentation.


2. Data Analytics: Engineering and IT teams are using MATLAB to build today’s advanced Big Data Analytics systems ranging from predictive maintenance and telematics to advanced driver assistance systems and sensor analytics. Teams select MATLAB because it offers essential capabilities not found in business intelligence systems or open source languages:

  • Physical-world data: MATLAB has native support for sensor, image, video, telemetry, binary, and other real-time formats. Explore this data using MATLAB MapReduce functionality for Hadoop, and by connecting interfaces to ODBC/JDBC databases.
  • Machine learning, neural networks, statistics, and beyond: MATLAB offers a full set of statistics and machine learning functionality, plus advanced methods such as nonlinear optimization, system identification, and thousands of prebuilt algorithms for image and video processing, financial modeling, control system design.
  • High speed processing of large data sets. MATLAB’s numeric routines scale directly to parallel processing on clusters and cloud.
  • Online and real-time deployment: MATLAB integrates into enterprise systems, clusters, and clouds, and can be targeted to real-time embedded hardware.


3. Internet of Things: Internet of Things (IoT) describes an emerging trend where a large number of embedded devices (things) are connected to the Internet. These connected devices communicate with people and other things and often provide sensor data to cloud storage and cloud computing resources where the data is processed and analyzed to gain important insights. Cheap cloud computing power and increased device connectivity is enabling this trend.

IoT solutions are built for many vertical applications such as environmental monitoring and control, health monitoring, vehicle fleet monitoring, industrial monitoring and control, and home automation. MATLAB® and Simulink® products support IoT systems by helping you develop and test smart devices, access and collect data in the cloud, and analyze IoT data.

At a high level, many IoT systems can be described using the diagram above. The left side of the diagram illustrates the smart devices (the “things” in IoT) that live at the edge of the network. These devices collect data and include things like wearable devices, wireless temperatures sensors, heart rate monitors, and hydraulic pressure sensors. The middle of the diagram represents the cloud where data from many sources is aggregated and analyzed in real time, often by an IoT analytics platform designed for this purpose. The IoT platform collects, processes, and stores data from the smart devices that are often geographically dispersed, and it may have the capability to analyze and take action on the incoming data.

The right side of the diagram depicts the algorithm development associated with the IoT application. Here an engineer or data scientist tries to gain insight into the collected data by performing historical analysis on the data. In this case, the data is pulled from the IoT platform into a desktop software environment to enable the engineer or scientist to prototype algorithms that may eventually execute in the cloud or on the smart device itself.


4. Motor and Power Control: Engineers developing motor control, battery management, and power conversion systems reduce their efforts by using MATLAB®, Simulink®, and Model-Based Design. These engineers develop their software algorithms before implementing them in hardware by:

  • Validating control algorithms through desktop and real-time simulation of the system dynamics
  • Optimizing system behavior using model libraries of energy sources and loads, power semiconductors, and a variety of circuit elements
  • Eliminating design problems found using simulation before moving to implementation
  • Testing and verifying designs with MATLAB and Simulink test harnesses
  • Generating HDL or C code from models for prototyping and implementation
  • Reusing models to speed up design iterations and next-generation projects

5. Wireless Technologies: Wireless engineering teams use today’s MATLAB® to reduce development time, from algorithm development through full system simulation and hardware implementation. These engineers save time and eliminate steps by:

  • Proving algorithm concepts in simulation and over-the-air tests  
  • Exploring and optimizing system behavior with models that include digital, RF, antenna elements
  • Eliminating design problems before moving to implementation
  • Streamlining testing and verification with MATLAB and Simulink® test harnesses
  • Automatically generating HDL or C code for prototyping and implementation
  • Reusing models to speed up design iterations and next-generation projects

Teams report saving as much as 30% in overall development time and 85% in functional verification time, having fewer design re-spins, and creating defect-free FPGA and ASIC implementations on the first attempt.


6. Computer Vision and image Processing: Algorithm development is central to image processing and computer vision because each situation is unique, and good solutions require multiple design iterations. MathWorks provides a comprehensive environment to gain insight into your image and video data, develop algorithms, and explore implementation tradeoffs. With MATLAB® and Simulink® products for image processing and computer vision, you can:

  • Acquire images and video from imaging hardware
  • Use graphical tools to visualize and manipulate images and video
  • Develop new ideas using libraries of reference-standard algorithms
  • Migrate designs to embedded hardware

7. Signal Processing: Signal processing is essential for a wide range of applications, from data science to real-time embedded systems. MATLAB® and Simulink® products make it easy to use signal processing techniques to explore and analyze time-series data, and they provide a unified workflow for the development of embedded systems and streaming applications.

With MATLAB and Simulink signal processing products, you can:

  • Acquire, measure, and analyze signals from many sources.
  • Design streaming algorithms for audio, smart sensor, instrumentation, and IoT devices.
  • Prototype, test, and implement DSP algorithms on PCs, embedded processors, SoCs, and FPGAs.


8. Robotics: Three of the most critical questions that robotics engineers and scientists need to answer are:

  • How do I design and simulate a robot?
  • How do I prototype and test algorithms for my robot?
  • How do I connect to my robot platforms and peripherals?

MATLAB® and Simulink® can help answer these questions, and accelerate and streamline the design, prototyping, and verification of robotics applications.


9. Control Systems: MathWorks tools for control system design support each stage of the development process, from plant modeling to deployment through automatic code generation. Their widespread adoption among control engineers around the world comes from the flexibility of the tools to accommodate different types of control problems. If your control problem is unique, you can create a custom tool or algorithm using MATLAB®.


10. Quantitative Finance and Risk Management: MATLAB® helps financial organizations develop quality-assured, transparent, documented, and replicable risk and stress testing models in days, not years. MATLAB offers agility amidst rapidly changing regulatory and business environments. With MATLAB, risk analysts and managers work with developers, integrators, stakeholders, and CROs toblend, scale, and customize research. You can incorporate “risk-aware” developer best practices when implementing model control and automation, reducing model and operational risk.

Using MATLAB, a single risk model stack can service multiple compliance regimes and front and middle office functions. On a smaller scale, you can customize, control, and automate market, credit, economic capital, and systemic risk models. You can operate alongside or validate existing vendor models, home-grown code, and spreadsheets.

  • Risk Modeling
  • Risk-Aware Development
  • Enterprise Implementation


Success Stories: Industries around the World Using MATLAB


Course Dates: 
Saturday 9 December – Sunday 10 December 2017


Course Time: 9 am to 4 pm


Prerequisites: Basic IT skills. A first year undergraduate-level mathematics or background in engineering, physics, mathematics, economics, finance or another applied science with some mathematical content will be helpful. No previous programming experience is required. However, it will be beneficial if you have programming experience in any other high-level programming language like C, C++ or Java – in which case, learning MATLAB will be like a fun for you.



  Information on how to book one or more of our courses can be found here .


Availability Type: 
Key Features
Assumes no prior knowledge or experience of programming
Using scientifically relevant and practical examples throughout enables students to quickly put their knowledge into practice
Case studies in linear algebra, numerical methods, and optimization
Several examples from engineering, science and economics
Problem Analysis and Algorithm Design
Problem Coding Using MATLAB
Advanced Topics in MATLAB Programming
An Introduction to Linear Algebra with MATLAB
An Introduction to Numerical Methods with MATLAB
An Introduction to Optimization with MATLAB