Data Science, Machine Learning and AI

Data Science and AI

An advanced data science and AI programme where students work with real-world datasets, build machine learning models, explore deep learning and generative AI, and develop responsible AI solutions through hands-on projects.

Age12 - 16

Duration8 months

FormatOne 2-Hour Session Per Week

Pathway StageLevel 03: Specialisation

Focus Areas

Foundations of Data Science

Data Engineering & Visualization

Machine Learning Fundamentals

Responsible AI & Decision Systems

Deep Learning & Computer Vision

Generative AI & Large Language Models

Capstone Project Development

Learning Outcomes

Strategy & Decisions

Understand how data science is used to solve real-world problems through structured workflows.

Clean Code

Clean, prepare, transform, and analyse real-world datasets using programming and database tools.

Strategy & Decisions

Create visual dashboards and communicate insights using data storytelling techniques.

Build and evaluate

Build and evaluate machine learning models for prediction, classification, clustering, and pattern recognition.

Explore deep learning,

Explore deep learning, computer vision, generative AI, chatbots, and AI-powered applications.

Apply ethical AI

Apply ethical AI principles including bias, fairness, responsibility, and model evaluation when designing AI systems.

Tools Used

Python

Google Colab

Jupyter

SQL Databases

Power BI

Scikit-learn

TensorFlow

Kaggle

Generative AI APIs & Tools

Prerequisites

Strong knowledge of Python programming is recommended

Students should be able to write and debug code independently and understand basic mathematical concepts such as averages, mean, median, and simple data interpretation

Completion of the Coding and Software Learning Path or equivalent coding experience is recommended

Students without prior Meu Labs experience can request an entry test to assess readiness

Course Structure

Guided hands-on coding sessions where students learn through real-world datasets, instructor explanations, interactive coding, model building, testing, and reflection

Continuous progress tracking through completed data projects, instructor feedback, coding milestones, analytical thinking, model performance, ethics understanding, and communication skills

Portfolio and certification outcomes with student work documented through dashboards, machine learning models, AI applications, Kaggle-style challenges, capstone projects, showcases, and a course completion certificate

Advanced project-based learning with real-world datasets, gamified challenges, competitions, continuous feedback, and an ethics-first AI development approach