σInteractive visual
Normal Distribution Explorer
Change the mean and standard deviation and watch the distribution respond instantly.
MeanStandard deviationProbability
Preview lab
Learn by doingMake difficult ideas
Make difficult ideas
visible.
Interactive Labs help you explore concepts rather than simply read about them. Change parameters, test ideas and see the consequences immediately.
Visual learningInteractive simulationsApplied intuition
Live concept preview
InteractiveExplore how data shape a model.
Parameter
Result0.78
Why interactive learning?
Understanding improves when you can change the system yourself.
Interactive learning turns abstract ideas into something you can manipulate, observe and reason about.
01
Change
Manipulate parametersAdjust values, assumptions and inputs instead of seeing only one fixed example.
02
Observe
See immediate consequencesWatch distributions, models, algorithms and visualisations respond in real time.
03
Explain
Build intuitionAsk why the result changed and connect the visual behaviour to the underlying theory.
04
Apply
Transfer the ideaMove from the interactive example to exercises, code, datasets and independent problem solving.
Interactive Lab directory
Explore concepts across all five disciplines.
Labs are organised around ideas that become easier to understand when you can see them respond visually.
σ
Explore subjectReason with evidence
Statistics
σSimulation
Confidence Interval Simulator
Repeatedly sample from a population and see what confidence level really means.
SamplingConfidence intervalsCoverage
Preview lab
σInteractive visual
Regression Playground
Move data points, change relationships and see how the regression line and residuals respond.
RegressionResidualsCorrelation
Preview lab
∑
Explore subjectBuild the foundations
Mathematics
∑Interactive visual
Derivative Visualiser
Move along a function and see the tangent slope change in real time.
FunctionsDerivativesRates of change
Preview lab
∑Visual mathematics
Matrix Transformation Lab
See how matrices rotate, stretch, reflect and transform points in two-dimensional space.
MatricesLinear transformationsGeometry
Preview lab
◉
Explore subjectTurn data into insight
Data Science
◉Machine learning
Gradient Descent Explorer
Watch an optimisation algorithm move across a loss surface towards a minimum.
OptimisationLoss functionsLearning rate
Preview lab
◉Machine learning
Classification Boundary Lab
Change training data and observe how a classifier separates regions of feature space.
ClassificationDecision boundariesModel complexity
Preview lab
⌬
Explore subjectDecode biological data
Bioinformatics
⌬Bioinformatics
Sequence Alignment Explorer
Compare biological sequences and explore how matches, mismatches and gaps affect alignment.
DNA sequencesAlignmentSimilarity
Preview lab
⌬Transcriptomics
Gene Expression Explorer
Explore expression values across samples and see how biological patterns emerge from high-dimensional data.
Gene expressionSamplesNormalisation
Preview lab
</>
Explore subjectThink computationally
Computer Science
</>Algorithms
Sorting Algorithm Visualiser
Watch different sorting algorithms operate step by step and compare their behaviour.
SortingComplexityAlgorithms
Preview lab
</>Computer Science
Data Structure Explorer
Interact with stacks, queues, trees and graphs to understand how they store and organise information.
StacksQueuesTrees
Preview lab
Connected learning
Labs are not separate from courses.
They are designed to sit between explanation and formal practice, helping difficult ideas become intuitive before you apply them independently.
01
Learn
Start with a structured explanation inside a course.
→
02
Explore
Use an Interactive Lab to manipulate the concept.
→
03
Practise
Solve problems and work through examples independently.
→
04
Apply
Use the concept in code, exams, projects or research.
Where this can grow
A full library of interactive understanding.
The lab system can eventually expand beyond demonstrations into coding environments, simulations, calculators and structured practice.
σ
Statistical simulations
Sampling, distributions, hypothesis testing, regression and probability.
∑
Mathematical visualisers
Functions, calculus, vectors, matrices and optimisation.
◉
Machine learning sandboxes
Classification, regression, clustering and model evaluation.
⌬
Bioinformatics explorers
Sequences, expression data, genomics and transcriptomics.
</>
Algorithm visualisers
Sorting, graph traversal, recursion and data structures.
▶
Guided coding environments
Run code, change examples and learn through experimentation.
Learn activelySee it.
See it.
Change it.
Understand it.
Combine courses with visual exploration and expert support to make difficult quantitative and computational ideas easier to understand.