THE INTERACTIVE CATALOGUE

Explore by doing.

A curated collection of deeper demonstrations, practical workbenches and cultural learning tools. Start with a question, choose one experience, and inspect the evidence behind its output.

Find a toolExplore a theme

26 selected tools and atlas collections

Applied AI

Welcome Week Studio

Survey coverage, trend and next-cohort scenarios.

Applied AI

Licence Compass

Dormancy rules, exceptions, capacity and demand.

Applied AI

Feedback Lens

Text themes with visible word evidence.

Applied AI

Staff Survey Explorer

Fictional cohort signals and model sensitivity.

Applied AI

NSS Pipeline Studio

Quality gates, deliberate faults and a forecast pipeline.

BA practice

BA Data Consultancy Workbench

Eight scenarios, four levels and downloadable delivery plans.

BA practice

BA Project Compass

A fictional workflow, evidence gates and value themes.

BA practice

Persona & Journey Studio

Sixteen personas, journeys, comparison and governance.

BA practice

BA Service Studio

Forty-eight activities and a selectable service pack.

Learning

AI & Data Jargon Buster

123 searchable terms, comparison and flashcards.

Cultural AI

Tone & character lab

Inspect Unicode and the loss caused by stripping marks.

Cultural AI

Whose voices are represented?

Compare weighted and equal-group evaluation.

Cultural AI

Text comparison bench

Edit distance, tone sensitivity and the limits of error rates.

Applied AI

PO Delay Predictor

A fitted fictional-data model with explainable outputs and evaluation limits.

Applied AI

Approval Delay Predictor

A fitted fictional-data model with explainable outputs and evaluation limits.

Applied AI

Anomaly / Fraud Review

A fitted fictional-data model with explainable outputs and evaluation limits.

Applied AI

Carbon Impact Predictor

A fitted fictional-data model with explainable outputs and evaluation limits.

Yorùbá learning

Àmì ohùn · See the melody

Explore the scenario, its output and the explanation of its method.

Yorùbá learning

Two columns. 256 possibilities.

Explore the scenario, its output and the explanation of its method.

Yorùbá learning

Ọmọlúàbí in a difficult moment

Explore the scenario, its output and the explanation of its method.

Maps

Yorùbáland interactive atlas

Towns, scale comparisons, landscape, satellite and natural-resource views.

Maps

Ọ̀yọ́’s imperial geography

Historical reach, neighbouring states and modern-border context.

BA foundations

Evidence Chain Studio

Six fictional cases connect needs, owners, data, acceptance and benefits evidence.

BA foundations

Adoption Capacity Planner

Compare facilitator time, group sizes and role-based learning plans.

Yorùbá learning

Òrìṣà · Living traditions explorer

32 sourced entries, age-friendly reading paths, comparison notes and a discovery trail.

Maps

Yorùbáland · Development maps

65 sourced landscape, resource and capability locations; filters, place comparisons and connected evidence reviews.

ADOPTION / FROM CURIOSITY TO USEFUL WORK

Let the tool guide you.

Choose Guide me ◇ beside an interactive tool. Start with the Quick path, or choose All controls for a detailed walkthrough. The guide highlights the next control, explains what to do and helps you interpret the result. You can minimise, close or restart it at any time.

How I design for adoption—and what would demonstrate value

A useful tool needs more than features. It needs a clear purpose, a safe example, understandable feedback and a way to apply what was learned. These walkthroughs help visitors change inputs, compare results, read limitations and take useful outputs onward.

Measure	What it would tell us
Task success	Can a visitor reach the intended outcome without assistance? Test a defined task with representative users.
Time to first useful result	How long from starting to a result the visitor can explain and use?
Understanding	Can the visitor explain what changed and identify a limitation?
Reported usefulness and return use	Does the tool help with a real need, and is it worth revisiting? Gather evidence with appropriate permission.

Guide clicks alone do not prove adoption. The current guide keeps only anonymous interaction counts in the visitor’s current browser session, with an optional local download. It sends no analytics and does not claim website-wide usage or improvement figures. Use the feedback page to share what helped or where you got stuck.

Personal tool suggestions

Enable JavaScript to choose optional feedback-based suggestions.

EXPLAINABLE AI & DATA / READ THE METHOD

Connect the learning to the evidence.

Use the categories to distinguish a capability, an algorithm, a Python package and a delivery step. A mention in a module or case study does not mean that capability is running on this page.

Follow the reasoning.

QuestionChoose the decision to support
MethodMatch the task to a suitable approach
TestCompare with appropriate evidence
ExplainState limits and human responsibility

Explore the building blocks.

AI Capability4

AI/Data Concepts1

Data Science Pipeline1

Evaluation & XAI2

Python Package3

Responsible AI1

Number of concepts in this guide, not performance or usage measures.

Explore the explanation matrix · 12 concepts

Concept categoryFind a term or example

12 concepts shown

On small screens, scroll the table sideways. “Learning context” explains a related idea; it does not mean that method runs on this page. Deep learning is the standard term for “deep ML”.

Technical term	Technical description / definition	In plain English	How this page uses it
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.
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.	Method guide The learning-to-practice path connects framing, preparation, modelling, evaluation and communication.
Features and labelsAI/Data Concepts	Features are model inputs; labels are target values or categories used in supervised learning.Primary reference ↗	What the model sees, and the answer it is learning to estimate.	Learning context Projects define relevant input features and any target labels before choosing an estimator.
Held-out evaluationEvaluation & XAI	Assessment on examples excluded from fitting and model selection, according to the declared split.Primary reference ↗	Keep some answers out of practice so the test means something.	Learning context Unseen and appropriately separated cases help assess whether a learned pattern generalises.
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 Explain what was actually done and what the available evidence supports.
NumPyPython Package	A Python library for multidimensional arrays and numerical operations.Primary reference ↗	The numerical toolbox behind many Python calculations.	Learning context A numerical Python library; it is not an AI capability or an algorithm by itself.
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 tabular Python library; a useful preparation tool, not a prediction model.
scikit-learnPython Package	A Python library providing estimators, preprocessing tools and model-evaluation utilities.Primary reference ↗	A toolkit of models and fairer ways to test them.	Learning context A Python library of algorithms and evaluation tools, distinct from each fitted model.
Human oversight and consentResponsible AI	People retain appropriate review authority, permission controls and correction routes for data use and outputs.Primary reference ↗	Keep someone able to question, correct or stop the result.	Responsible use Decision ownership, permission and correction belong throughout the workflow.