MSc Artificial Intelligence, Data Science and Sustainability

Module One: Global Sustainability Challenges and Strategic Responses

1. Globalization, Global Market Place, Development and Sustainability Transitions
2. Foundations of Sustainability and SD
3. The Evolution of the Concept of Development in the History of the UN System
4. Understanding the UN 2030 Agenda and SDGs
5. Human Capital for Sustainability: Education and Health Systems
6. Climate Governance, Environmental Risk, and Corporate Sustainability
7. Energy Transition: Renewable Energy and the Nuclear Debate
8. The AI Carbon Footprint: Data Centres, E-Waste, and Resource Scarcity
9. Sustainable Urban Development, Heritage and Tourism
10. Accounting and Financial Literacy for Non-Finance Managers

Module Two: Applied Data Science, Machine Learning and Statistical Modelling

1. Foundations of Probability, Statistical Inference, and Predictive Analytics
2. Exploratory Data Analysis (EDA) and Visualisation for Environmental Metrics
3. Supervised Learning: Regression and Classification for Sustainability Forecasting
4. Unsupervised Learning: Clustering and Dimensionality Reduction in Ecological Data
5. Time-Series Analysis for Climate Change Modelling and Resource Management
6. Scalable Data Engineering: Pipelines and Database Management Systems
7. Bayesian Networks and Probabilistic Graphical Models for Risk Assessment
8. Data Wrangling and Pre-processing for Messy Real-World Impact Data
9. Evaluating Model Performance: Bias, Variance, and Generalisation in Scientific AI
10. Quantitative Research Methods and Evidence-Based Decision Making

Module Three: AI Architectures, Neural Networks and Green Computing

1. Introduction to Neural Networks and Deep Learning Architectures
2. Computer Vision for Satellite Imagery and Biodiversity Monitoring
3. Natural Language Processing (NLP) for Global Policy Analysis and ESG Reporting
4. Edge AI and IoT: Deploying Intelligent Sensors in Remote Ecosystems
5. Green AI: Optimising Algorithmic Efficiency and Computational Complexity
6. Sustainable Infrastructure: Reducing the Energy Demand of Large Language Models
7. Hardware Acceleration and Carbon-Aware Computing Strategies
8. Reinforcement Learning for Smart Grid Management and Energy Optimisation
9. Digital Twins: Simulating Sustainable Urban and Industrial Systems
10. Generative Models for Synthetic Data in Rare Event Climate Simulation

Module Four: Algorithmic Governance, Ethics and Data-Driven Sustainable Policy

1. AI Ethics: Transparency, Accountability, and Algorithmic Fairness
2. Data Privacy and Security in the Age of Global Information Exchange
3. Regulatory Frameworks: Navigating the EU AI Act and International Standards
4. Human-Centred AI: Inclusivity and Social Equity in Automated Systems
5. Governance of Autonomous Systems in Critical Infrastructure and Conservation
6. Intellectual Property, Data Ownership, and Open-Source for Global Good
7. Auditing Algorithms for Environmental and Social Impact
8. Data Sovereignty and the Digital Divide in Developing Nations
9. Leadership in Digital Transformation: Managing Technical Teams with Ethical Vision
10. Corporate Digital Responsibility (CDR) and the Future of Sustainable Tech Policy

Module Five: Capstone Project

The Capstone Project represents the culmination of the programme, requiring students to integrate their technical expertise in AI and DS with a robust commitment to sustainable development.

Key Stages:

1. Project Proposal Development – Formulation of a rigorous research or technical consultancy proposal addressing a real-world environmental, social, or industrial sustainability challenge using computational methods.
2. Literature Review – Critical review of academic literature and industry white papers on machine learning architectures, data ethics, and ecological stewardship.
3. Methodology and Model Design – Selection of appropriate computational frameworks, including the design of neural networks, data pipelines, or statistical models, integrated with ethical AI guidelines and green computing principles.
4. Data Acquisition and Processing – Application of advanced data engineering techniques to collect, clean, and manage large-scale datasets, including the use of APIs, remote sensing, or open-source repositories.
5. Analysis and Model Validation – Rigorous testing of AI models using quantitative metrics to generate deep insights into sustainability impacts, resource optimisation, or predictive forecasting.
6. Findings and Discussion – Interpretation of computational results in relation to algorithmic efficiency, digital innovation, and measurable progress toward the Sustainable Development Goals (SDGs).
7. Conclusions and Technical Recommendations – Development of practical, evidence-based technical solutions and policy recommendations for organisations seeking to deploy AI responsibly.
8. Publication and Dissemination – Preparation of the project findings for professional technical presentation, peer-reviewed publication, or open-source contribution to demonstrate applied impact in the fields of AI and sustainability.