Practical MATLAB Skills Course

Lesson 1 • Navigating the MATLAB Desktop

Covers the Command Window, Editor, Workspace, and Current Folder panels. Establishes the working environment needed for all subsequent coding tasks.

Lesson 2 • Getting Help and Using Documentation

Demonstrates the help, doc, and lookfor commands alongside online resources. Enables self-sufficient problem-solving throughout the course.

Lesson 3 • Writing and Running Scripts

Teaches script creation, saving, and execution from the Editor. Connects syntax knowledge to reproducible, file-based workflows.

Lesson 1 • Array Arithmetic and Broadcasting

Explains element-wise versus matrix operators and implicit expansion rules. Enables compact numerical computations without explicit loops.

Lesson 2 • Vectorisation Strategies

Teaches replacing scalar loops with vectorised expressions for speed and clarity. Directly prepares students for efficient data processing in later chapters.

Lesson 3 • Built-in Array Functions

Surveys size, length, reshape, sort, sum, and related utilities. These functions form the toolkit for rapid data manipulation throughout the course.

Lesson 1 • Writing Custom Functions

Explains function files, input/output arguments, and local scope. Modular functions are the building block of all advanced scripts in this course.

Lesson 2 • Loops and Iteration

Teaches for and while loops, break, continue, and loop performance considerations. Builds iteration skills needed for data processing and simulations.

Lesson 3 • Anonymous and Nested Functions

Introduces anonymous function handles and nested function definitions. Enables compact, inline computations used in optimisation and plotting callbacks.

Lesson 4 • Error Handling and Debugging

Covers try-catch blocks, error and warning messages, and the MATLAB debugger. Builds robust code practices essential for professional-quality scripts.

Lesson 1 • Connecting to External Data Sources

Surveys database connectivity, web API calls, and reading binary formats. Broadens data-access skills for professional data engineering tasks.

Lesson 2 • Reading Tabular Data Files

Covers readtable, readmatrix, and csvread for CSV, Excel, and text files. Establishes the primary data-ingestion skills used throughout applied chapters.

Lesson 3 • Writing and Saving Data

Teaches writetable, writematrix, and MAT-file saving with save and load. Ensures processed results can be stored and shared reliably.

Lesson 4 • Handling Missing and Inconsistent Data

Addresses NaN detection, removal, and imputation strategies within MATLAB. Clean data pipelines are prerequisite for accurate analysis in later chapters.

Lesson 1 • Core 2-D Plot Types

Covers plot, scatter, bar, histogram, and area charts with formatting options. These chart types handle the majority of professional data communication needs.

Lesson 2 • Formatting and Annotating Figures

Teaches axis labels, titles, legends, colour, line style, and text annotations. Proper formatting transforms raw plots into professional deliverables.

Lesson 3 • Subplots and Figure Layouts

Explains subplot, tiledlayout, and figure sizing for multi-panel figures. Enables side-by-side comparisons essential in analytical reports.

Lesson 4 • Exporting and Automating Figures

Covers exportgraphics, print, and scripted figure generation for batch output. Automates reporting workflows introduced in the data management chapter.

Lesson 1 • Numerical Integration and Differentiation

Covers integral, integral2, trapz, and numerical gradient computation. Enables area-under-curve and rate-of-change calculations for applied problems.

Lesson 2 • Root Finding and Optimisation

Introduces fzero, fsolve, fminbnd, and fminunc for scalar and multivariate problems. Builds solver skills applied directly in the simulation chapter.

Lesson 3 • Interpolation and Curve Fitting

Teaches interp1, interp2, spline, and polyfit for data approximation. Connects raw data to smooth models used in visualisation and simulation.

Lesson 4 • Linear Algebra Operations

Covers matrix factorisation, solving Ax=b, eigenvalues, and condition numbers. Provides the mathematical backbone for simulation and optimisation chapters.

Lesson 1 • Digital Filtering

Covers FIR and IIR filter design, application, and frequency response analysis. Provides noise-reduction tools applied to sensor data in the project chapter.

Lesson 2 • Time-Series Analysis

Teaches moving averages, detrending, autocorrelation, and resampling techniques. Prepares students to analyse sensor, financial, and experimental time data.

Lesson 3 • Descriptive Statistics and Distributions

Covers mean, std, median, percentile, and histogram-based distribution analysis. Establishes statistical literacy needed for all data interpretation tasks.

Lesson 1 • Designing Modular MATLAB Projects

Covers project folder structure, function libraries, and dependency management. Transforms isolated scripts into maintainable, team-ready codebases.

Lesson 2 • End-to-End Capstone Project

Students build a complete pipeline from raw data ingestion through analysis to a formatted report. Consolidates every skill from all eight core chapters.

Lesson 3 • Automated Reporting with Live Scripts

Teaches Live Script formatting, embedded outputs, and export to PDF and HTML. Enables reproducible, self-documenting analytical reports.