Applied ML & Data Science
Hagnýtt vélanám og gagnavísindi
Real data, real models, real judgement.
Book a free trialWhat they'll learn
- Real, messy data end to end Take an actual dataset from raw mess to finished result, the whole pipeline.
- Features and model choice Engineer useful features and choose the right model for the problem.
- Honest evaluation Evaluate rigorously and document limits with proper model cards.
- Ethics and the EU AI Act Apply real AI ethics and the EU AI Act to the work, not just in theory.
A full data-science project on a real dataset, documented with a professional model card.
The 13-week journey
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Sprint 1 — The data pipeline
Messy real data: cleaning, missing values, leakage; exploratory analysis and visualisation; framing a question a model can answer. Ends with a cleaned dataset and a clear question.
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Sprint 2 — Modelling well
Feature engineering, model selection, cross-validation, and choosing metrics that fit the problem; comparing models honestly. Ends with a tuned, defensible model.
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Sprint 3 — Responsible & done
Bias analysis, the EU AI Act and data-protection basics, and a professional model card; packaging the project for a portfolio. Ethics through real cases.
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Week 13 — Showcase
Project presentations with results, limitations and a portfolio-ready write-up.
What we cover
Every topic, unit by unit — so you know exactly what your child builds and learns.
The data pipeline
- Cleaning messy data, missing values and leakage
- Exploratory analysis and visualisation
- Framing a question a model can answer
- A cleaned dataset and a clear question
Modelling well
- Feature engineering
- Model selection and cross-validation
- Choosing metrics that fit the problem
- Comparing models honestly
Responsible & done
- Bias analysis on real data
- EU AI Act and data-protection basics
- Writing a professional model card
- Packaging for a portfolio
Showcase & portfolio
- Presenting results and limitations
- A portfolio-ready write-up
- Project presentations to the audience
A real-world data project with its analysis, model and honest evaluation — presented like a junior data scientist.
The data-science track for teens who want depth: real, messy data taken from question to model to honest conclusion — with the evaluation rigour, ethics and documentation a professional brings. The course that turns AI enthusiasm into method.
The hooks
Teen hook: “I took a real dataset from mess to a model I can defend — and I know exactly what it can’t do.” Parent hook: “Genuine data-science skills and ethics — university-level substance, taught by building.”
Who it’s for
16–18s with Python and some ML (AI Engineer or Machine Learning Lab + placement). Thrives: the analytical, the rigorous, the future researchers and engineers.
Outcomes — by the end, students can
Clean and explore messy real data; engineer features and select models; evaluate with appropriate metrics and cross-validation; analyse bias; write a model card; situate work within the EU AI Act and data-protection basics.
Tools & compliance
Python notebooks, pandas/scikit-learn (and a deep-learning taster), real and open datasets, own accounts at 16+; ethics, data-protection and honest-evaluation norms enforced.
Where this course fits
A technical companion to AI Engineer; strong preparation for a data-driven Launchpad capstone.
Parent questions
How is this different from AI Engineer?
AI Engineer spans ML, neural nets and LLM apps; this goes deeper on the data-science craft — messy data, feature work, evaluation and ethics.
Is it useful for university?
Very — a documented data project and honest evaluation are exactly what STEM and CS applications value.
What background is needed?
Python and some ML (AI Engineer or Machine Learning Lab, or placement).
The first lesson is a free trial.
Book a no-commitment trial — pay nothing if it's not a fit.
Book a free trial