Modelling Course

Lesson 1 • Ethics and Responsibility in Modelling

Covers bias, transparency, and accountability when models influence real decisions. Grounds professional practice in ethical standards.

Lesson 2 • Types of Models Overview

Surveys conceptual, mathematical, statistical, and simulation models. Helps students match model type to problem context.

Lesson 3 • What Modelling Is and Why It Matters

Defines modelling and its purpose across industries. Establishes shared vocabulary used throughout the course.

Lesson 4 • Assumptions and Simplifications

Explains how assumptions shape model behaviour and where simplifications introduce risk. Students learn to document and justify every assumption.

Lesson 1 • Matrix and Network Representations

Explains matrices and graph structures as compact model representations. Prepares students for system and network models in advanced chapters.

Lesson 2 • Rates of Change and Optimisation

Covers derivatives and optimisation concepts without requiring advanced calculus. Students identify maxima, minima, and sensitivity in model outputs.

Lesson 3 • Essential Algebra for Modellers

Reviews variables, equations, and functions as modelling building blocks. Directly supports formula construction in later chapters.

Lesson 4 • Probability and Uncertainty Basics

Introduces probability distributions, expected value, and variance. Provides the foundation for stochastic modelling introduced in later chapters.

Lesson 1 • Identifying Data Requirements

Maps model inputs to required data types and sources. Prevents common failures caused by misaligned data and model design.

Lesson 2 • Data Collection Methods

Surveys surveys, experiments, observation, and automated collection. Students select appropriate methods for their modelling context.

Lesson 3 • Data Governance and Documentation

Establishes standards for data provenance, versioning, and access control. Ensures reproducibility and auditability of model inputs.

Lesson 4 • Exploratory Data Analysis

Uses descriptive statistics and visualisation to understand data before modelling. Reveals patterns, distributions, and anomalies early.

Lesson 1 • Lookup and Conditional Logic

Incorporates rule-based switching and table lookups into model calculations. Enables models to handle discrete categories and policy rules.

Lesson 2 • Structuring a Deterministic Model

Defines inputs, calculations, and outputs in a logical layout. Good structure reduces errors and makes models easier to audit.

Lesson 3 • Model Auditing and Error Checking

Applies systematic checks to detect formula errors, broken links, and logic flaws. Auditing is a professional standard before any model is shared.

Lesson 4 • Nonlinear and Ratio-Based Models

Extends model logic to multiplicative, exponential, and ratio relationships. Captures real-world dynamics that linear models cannot represent.

Lesson 1 • Model Selection and Validation

Applies information criteria, cross-validation, and holdout testing to choose the best model. Prevents overfitting and ensures generalisability.

Lesson 2 • Multiple Regression Models

Extends regression to multiple predictors and controls for confounding. Students manage multicollinearity and interpret partial effects.

Lesson 3 • Model Diagnostics and Assumptions

Tests linearity, homoscedasticity, normality, and independence of residuals. Violations are diagnosed and corrected before model use.

Lesson 4 • Categorical Variables and Interactions

Encodes categorical predictors and models interaction effects. Expands the range of real-world problems regression can address.

Lesson 1 • System Dynamics and Feedback Loops

Captures stocks, flows, and feedback in dynamic systems over time. Reveals counterintuitive behaviour caused by delays and nonlinearity.

Lesson 2 • Introduction to Stochastic Thinking

Contrasts deterministic and stochastic approaches and explains when randomness must be modelled. Motivates simulation as a practical tool.

Lesson 3 • Discrete-Event Simulation

Models systems as sequences of events with queues, resources, and timing. Applied to process optimisation and capacity planning.

Lesson 4 • Simulation Output Analysis

Applies statistical methods to simulation results to draw valid conclusions. Covers warm-up periods, run length, and confidence intervals.

Lesson 1 • Scenario and Stress Testing

Defines coherent scenarios and extreme stress cases to test model robustness. Prepares models for adverse and unexpected conditions.

Lesson 2 • Validation Concepts and Standards

Distinguishes verification, validation, and calibration and explains professional standards for each. Sets the quality bar for all model outputs.

Lesson 3 • Backtesting and Historical Validation

Tests model predictions against known historical outcomes to measure accuracy. Reveals systematic bias and structural weaknesses.

Lesson 4 • Global Sensitivity and Interaction Effects

Applies variance-based methods to measure combined input effects on output variance. Captures interactions missed by one-way analysis.

Lesson 1 • Data Visualisation for Model Results

Selects and designs charts that accurately represent model outputs and uncertainty. Visualisation choices directly affect stakeholder interpretation.

Lesson 2 • Structuring Model Outputs for Audiences

Tailors output format and depth to technical and non-technical stakeholders. Effective communication determines whether model insights drive action.

Lesson 3 • Model Documentation and Handover

Creates user guides, technical specs, and change logs for model handover. Documentation ensures continuity when ownership transfers.

Lesson 4 • Embedding Models in Workflows

Integrates models into business processes via APIs, dashboards, or automated pipelines. Operational embedding maximises model impact.