Vectorisation exercise
Deliverable: A loop-based script rewritten in vectorised form, with execution times before and after recorded and the speedup explained.
Course
MATLAB and Simulink
MATLAB taught as an engineering tool — vectorised computation, signals, control and Simulink — with the habit of checking a simulation against something you already know.
- Duration: 3 months
- Classroom · Online live
- Level: intermediate
What you will be able to do
- Write vectorised MATLAB instead of slow loop-based code
- Import, clean and analyse experimental data
- Produce publication-quality plots with correct labelling
- Analyse signals in the time and frequency domains
- Model a dynamic system in Simulink and interpret its response
- Verify a simulation against an analytical result before trusting it
Who this course is for
- Engineering students and graduates, all disciplines
- Researchers analysing experimental data
- Working engineers in control, signals or power
- School-level calculus and basic programming are assumed
Syllabus
6 modules · 3 months
Module 1. MATLAB fundamentals
- Matrices, indexing and slicing
- Vectorisation and why loops are the wrong reflex
- Scripts, functions and code organisation
- Debugging and the workspace
Module 2. Data and visualisation
- Importing from files, instruments and spreadsheets
- Cleaning, interpolating and handling missing values
- Plot types and choosing one that answers the question
- Labelling, subplots and export for a report
Module 3. Numerical methods
- Solving linear systems
- Root finding and optimisation
- Numerical integration and differentiation
- Ordinary differential equations and solver choice
Module 4. Signal processing
- Sampling, aliasing and the Nyquist limit
- The FFT and reading a spectrum honestly
- Filter design and phase effects
- Windowing and spectral leakage
Module 5. Control systems
- Transfer functions and state space
- Step response, poles and stability
- PID design and tuning
- Bode and root locus, read rather than memorised
Module 6. Simulink
- Blocks, signals and solver settings
- Modelling a physical system
- Subsystems, masking and reusable models
- Simulation against measured data
Tools and technologies you will use
- MATLAB
- Simulink
- Signal Processing Toolbox
- Control System Toolbox
Projects you will build
Signal analysis with a verified spectrum
Deliverable: A real signal analysed with an FFT, with the identified frequencies confirmed against a known component of the signal and any aliasing discussed.
Controlled system in Simulink
Deliverable: A dynamic system modelled with a PID controller tuned to a stated specification, with the step response meeting overshoot and settling time targets.
Analytical cross-check
Deliverable: One simulation whose result is verified against a hand-derived analytical solution, with the agreement shown and any divergence explained rather than ignored.
Where this course can take you
- Design Engineer
- Control Systems Engineer
- Signal Processing Engineer
- Research Assistant
- Simulation Engineer
Duration, modes and fees
- Duration
- 3 months
- Delivery modes
- Classroom · Online live
- Fees
- Share your details for the current fee
- Fees vary by batch and delivery mode. Academic licences are used for practice sessions.
Placement assistance
Every student gets placement assistance — that is what 100% placement assistance means. It is support for all, not a job for all. We do not promise a specific salary, a specific number of interviews, or placement at any named company, and you should be wary of anyone who does.
What is included
- A place in the monthly placement drive, held every third Saturday
- The readiness programme every second Saturday — mock interviews and preparation
- CV review against the specific roles you are targeting
- Portfolio review, so your project work is presented the way a reviewer will read it
- Access to the vacancy pool employers send directly to the Samyak network
- Guidance on which roles realistically fit your background and which do not
- A place in the next drive, with coaching, if you are not selected in this one
What is not included
- Any guarantee of a job, an interview, or a particular salary
- Placement at a named or partner company
- Applying to jobs on your behalf
- Support before you have completed the course and its project work
- Visa, relocation or overseas placement assistance
A simulation always produces a plot
That is the danger. Wrong model, wrong parameters, wrong solver settings — the output is still a smooth, confident curve that looks like an answer.
So one project exists solely to build the checking habit. Take a system whose step response you can derive on paper, model it, and compare. If they disagree, the model is wrong and you find out in a classroom rather than in a design review.
Engineers who never form this habit produce work nobody can trust, including themselves.
Stop writing loops
Most people arrive from C or Python and write MATLAB with for loops.
It works, it is slow, and it is longer. MATLAB operates on whole matrices, and the same computation vectorised is frequently a hundred times faster and a third the length. The first project makes you rewrite your own script and record the timing, because seeing your own code speed up that much is what actually changes the habit.
Read a Bode plot, do not memorise it
Control theory taught as formulae to reproduce in an exam is forgotten by December.
Taught as things you can see — where the poles are, what the phase margin is doing, why the response overshoots — it stays. The control module is built around plots and around changing a parameter to watch what moves.
Where MATLAB is simply the answer
If your field models in Simulink, there is no real alternative and the Python-versus-MATLAB debate does not apply.
Automotive, aerospace, control and a great deal of academic research work this way, and their advertisements say so. If your work is general programming or machine learning, Python is the better investment and we will tell you that rather than sell you this.
Questions
MATLAB and Simulink — frequently asked questions
Is MATLAB worth learning when Python is free?
In academia, control engineering, signal processing and much of automotive and aerospace R&D, MATLAB and Simulink remain the working standard and job advertisements name them. Python is stronger for general programming and machine learning. If your field uses Simulink for modelling, there is no equivalent and the question does not really arise.
Do I need strong mathematics?
School-level calculus and comfort with matrices are enough to start. The control and signals modules use more, but they build it — poles, stability and spectra are taught as things you can see in a plot rather than derive from scratch. Engineering students are the natural audience.
Why does the course insist on verifying against analytical results?
Because a simulation always produces a plot, including when the model is wrong. A first-order system has a known step response; if your Simulink model disagrees, the model is wrong. Engineers who never build that checking habit ship confident nonsense, and this is the single most valuable discipline in the course.
What is the point of the vectorisation project?
To break the loop reflex that programmers bring from other languages. MATLAB operates on whole matrices, and code written that way is often ten to a hundred times faster and considerably shorter. Seeing your own script speed up by two orders of magnitude changes how you write from then on.
Enquire about MATLAB and Simulink
Three details is all we need. A course advisor will call you back.
Related courses
- Python Programming
Python taught as a working tool rather than a syntax tour — you finish able to read unfamiliar code, automate real tasks, call APIs and write tests that catch your own mistakes.
- Embedded Systems
Firmware written close to the metal — registers, interrupts, timers and buses — debugged on real hardware, because embedded bugs live where a simulator cannot show them.
- Ansys (Finite Element Analysis)
Finite element analysis taught around validation, because a colourful stress plot is easy to produce and only useful once you can show it is not nonsense.
- R Programming for Statistics
R taught for statistics rather than as a second Python — data wrangling, visualisation, testing and regression, with the discipline to interpret a result honestly.
Next step
Talk to a course advisor
Tell us what you want to learn and we will help you pick the right course, batch and mode.