LEARN · PRACTISE · BUILD

AI & Data Science Learning Studio

Four connected projects take you from raw data to responsible AI evaluation. Suitable for KS3 extension, GCSE preparation and independent post-16 exploration.

Choose a practical learning route

Start with messy data, build a prediction, explore a trained classifier and explain what the results justify. The activities use fictional or clearly credited educational datasets, run locally in the browser and collect no student details.

01 · Beginner · 20–30 minutes

Data Detective

Find missing values, repeated records and unusual numbers. Calculate means, compare cleaning decisions and keep an evidence trail.

Investigate a dataset →
02 · Developing · 30–45 minutes

Train & Test Studio

Fit a straight-line regression model using the fictional bike-hire dataset from Chapter 18, then compare it to a baseline on eight unseen days.

Train and evaluate →
03 · Extending · 35–50 minutes

Classification Lab

Use k-nearest neighbours to classify fictional sensor readings. Change k, inspect false alarms and missed positives, then evaluate limitations.

Explore a trained classifier →
04 · Consolidate · 25–40 minutes

Assessment & Portfolio

Complete original self-check questions and a printable project brief. Explain your cleaning, train/test separation, evaluation and ethics.

Test and evidence your learning →
Connected to the published TutorSyntax book

AI & Data Science Foundations — chapters 5–9, 13–14, 16–18 and 20 support the learning route.

Book downloads & details →

Take the learning further

Download and run Python

All three investigations come with matching commented Python programs and sample CSV files, plus a learner worksheet and teacher notes.

Download v26 project pack (ZIP) →

Plan a research project

Choose an appropriate research question, record provenance, compare baselines and document limitations. These tasks are EPQ preparation, not a qualification.

Open EPQ research starter →

Explore related careers

See where data analysis, model development and transparent evaluation are used professionally.

Data scientist profile →
Curriculum note

These are original TutorSyntax enrichment resources informed by emerging computing education themes. They are not official Ada, Raspberry Pi Foundation, examination-board or EPQ assessment materials. Current GCSE specifications remain available elsewhere on TutorSyntax.