This module provides technical and academic orientation, familiarising participants with the programme structure, tools, and learning environment before the core curriculum begins.

This module establishes the conceptual foundations of data science, introducing generative AI alongside core machine learning and AI principles including bias, variance, prediction, and inference models.

This module builds Python programming skills for data science, covering language fundamentals, data structures, control flow, and key scientific computing libraries including NumPy, Pandas, SciPy, and Matplotlib.

This module covers end-to-end data preparation and visual analysis, including data extraction, cleaning, visualisation with Python and Tableau, regular expressions, and relational database querying with SQL.

This module introduces the machine learning project lifecycle and a range of core algorithms, spanning classification, regression, clustering, and tree-based methods with hands-on application.

This module advances machine learning expertise through ensemble methods, dimensionality reduction, and probabilistic clustering techniques including Gaussian Mixture Models for both clustering and data generation.

This module covers the fundamentals of text analytics, from preprocessing and frequency-based analysis to building ML models from text, language modelling with n-grams, and applying deep learning techniques including Word2Vec.

This module introduces artificial neural networks and deep learning frameworks, progressing from ANN fundamentals and Scikit-learn implementation to Keras and TensorFlow, covering both sequential and functional model architectures and performance optimisation.

This module covers the operational infrastructure for machine learning, including version control, Docker containerisation, and CI/CD pipeline implementation using GitHub Actions and AWS for model deployment.

This module introduces Generative AI fundamentals, equipping participants to design and build GenAI tools and custom GPTs through prompt engineering and advanced prompting techniques.

This specialisation track develops deep learning expertise across computer vision, sequential data processing, and NLP, culminating in advanced Generative AI topics including Transformers, RAG, AI agents, and LangChain.

This specialisation track covers large-scale data engineering on Azure and Apache ecosystems, from distributed storage and Spark-based ETL to streaming pipelines, MLlib feature engineering, and AI-assisted data transformation with GenAI integration.