| Visual encodingVisualisation | Mapping values or categories to marks, position, length, colour and labels.Primary reference ↗ | Make the comparison visible, with a scale and a clear label. | Published analysis
Read axis labels, denominators and captions before comparing exhibits. |
|---|
| Synthetic dataAI/Data Concepts | Generated records used to represent scenarios rather than directly report observed individuals or events.Primary reference ↗ | A practice dataset. Useful for demonstration, not proof of workplace impact. | Demonstration
Exhibits labelled synthetic or reconstructed illustrate methods; they are not disclosed workplace records. |
|---|
| Descriptive analyticsAI/Data Concepts | Summaries of observed data using counts, rates, distributions or comparisons.Primary reference ↗ | Tell us what the records show; this alone does not predict next week. | Published analysis
Counts and trend summaries describe the stated records and period, not future performance. |
|---|
| Residuals and prediction errorEvaluation & XAI | A residual compares an observed value with its fitted or predicted value. Error metrics summarise those discrepancies.Primary reference ↗ | How far did the estimate miss, and where does it miss most? | Learning context
Prediction exhibits can compare observed and estimated values; a smooth chart alone is not validation. |
|---|
| Confusion matrix, precision and recallEvaluation & XAI | A confusion matrix counts predicted versus actual classes. Precision measures positive-result reliability; recall measures positive-case coverage.Primary reference ↗ | Which alarms were right, which were false, and which cases were missed? | Learning context
Where a confusion chart is shown, inspect false alarms and missed cases as well as correct predictions. |
|---|
| Data science pipelineData Science Pipeline | An ordered, reproducible sequence for acquiring, validating, preparing, analysing and evaluating data.Primary reference ↗ | A chain from a question and source to an answer someone can check. | Published analysis
Case studies connect the problem, evidence, preparation, method, evaluation and decision. |
|---|
| Explanation versus causal proofEvaluation & XAI | A model explanation describes selected behaviour or contributions; it does not establish causal effects or full reliability.Primary reference ↗ | A reason for this output is useful, but it is not proof that the model is right. | Responsible use
A persuasive visual does not prove causation or transferability. |
|---|
| Data minimisationResponsible AI | Limit collected, disclosed and retained information to what is needed for the stated purpose.Primary reference ↗ | Use the smallest appropriate amount of information. | Responsible use
Restricted source records stay private; approved aggregates and labelled reconstructions support public learning. |
|---|
| pandasPython Package | A Python library for labelled tabular data manipulation and analysis.Primary reference ↗ | A programmable workbench for cleaning and comparing tables. | Learning context
A common Python option for tabular preparation; its inclusion is not a claim that this browser runs pandas. |
|---|
| MatplotlibPython Package | A Python plotting library for static, animated and interactive visualisations.Primary reference ↗ | Turn numerical work into labelled figures that can be inspected and shared. | Learning context
A common way to prepare Python figures. This page displays published assets rather than launching Python plotting sessions. |
|---|
| Machine learning (ML)AI Capability | Methods that estimate patterns or parameters from examples to support inference on other inputs.Primary reference ↗ | Learn a pattern from examples, then check whether it helps with new ones. | Learning context
ML learns from examples. Read the page-specific methods below to see whether any model runs here. |
|---|
| Deep learningAI Capability | Machine learning using neural networks with multiple representation-learning layers.Primary reference ↗ | A layered model learns useful representations; more layers do not guarantee a better answer. | Learning context
Deep learning is an AI capability, not a label for every chart. No deep model is implied by this explanation panel. |
|---|
| Natural language processing (NLP)AI Capability | Computational methods for representing, analysing or generating human language.Primary reference ↗ | Help a computer work with words, while remembering that context matters. | Learning context
Text search, translation, thematic classification and generation are different language tasks; their implementation is identified separately. |
|---|
| Sentiment analysisAI Capability | A language-analysis task that estimates an expressed attitude, often as polarity or sentiment categories.Primary reference ↗ | Estimate the tone of words—not the private feelings or personality of their author. | Learning context
Sentiment estimates expressed tone. It must not be confused with topic classification or a diagnosis of a person. |
|---|
