This module is designed to equip students with a critical understanding of the principles, techniques, technologies and applications of Data Science and AI specifically as they apply in the context of sustainability. Students will examine existing and potential future use of Data Science and AI across a range of real-world domains, including general society, business, health, education and culture, through the lens of meeting the UN Sustainable Development Goals. The strengths, weakness, opportunities and threats, including wider societal impacts and ethical issues will also be considered and addressed through practical problem solving and scenario-based work exercises. The module will move away from the traditional static lecturing style, bringing the students along a learning path via a problem-centred learning approach, as well as the use of case studies, in-class projects, discussions, student-led activities, and guest lectures. An additional aim of this module is to improve the students’ ability to interact, to present, to discuss, to think critically and to consider multi-faceted problems. A portion of marks will be awarded for participation in weekly project work sessions via the assessment strategy, as engagement will be a key consideration.
Introduction to Data Science and AI: Overview, history and key milestones, main application areas;
Sustainability: What is sustainability? UN Sustainability Goals, Types of Sustainability (Environmental, Social, Economic, Food, Water, and Energy), Criticisms of Sustainability and the UN Sustainability Goals.
Data Science: What is data and what are datasets, Cleaning and preprocessing datasets, Data Visualization, Important features (Feature Engineering), Evaluation of datasets.
Artificial Intelligence: What are the main areas, what are the underlying concepts, how do they work (high level), what can they do?: Including: Data-driven AI (machine learning), Large language models (e.g. ChatGPT, Bard ), Generative AI, Natural Language Processing, Computer Vision, Robotics, Virtual Reality, Metaverse, Knowledge Driven AI/Expert Systems, inspirations and link to neuroscience.
Real-world Deployment: Challenges of Data Science and AI including explainable AI, environmental factors , data privacy, data bias, human interaction and any other factors relevant to real-world development and deployment;
Ethics: Critical assessment of Data Science and AI including ethical and environmental issues
Using AI in real domains for real problems: Health, business, education, culture, society, legal, manufacturing, – real life case studies, future of work and roles, decision owning, benefits, opportunities, issues, use cases, addressing the sustainability goals
Industry and Academia: Overview of Data Science and AI development and research
Research Outputs: Exploring new directions in Data Science and AI, including overview of research publication space, patents filing; Identify credible reading and knowledge sources (e.g. peer reviewed articles, verified media versus social media media)/
| The module will move away from the traditional static lectures, bringing students along a learning path via problem-centred learning, case studies, in-class projects, discussions, student-led activities, guest lectures and site visits. The students will learn by using data science and AI , trying it out, creating demos, suggesting solutions, thinking critically. An additional aim of this module is to improve the students’ ability to interact, to present, to discuss, and to consider multi-faceted problems. A portion of marks will be awarded for participation in weekly sessions as engagement will be a key factor in this module. Class time will facilitate student team groups to present their work and demonstrate any of their outputs. By the end of the module, the students will have developed a portfolio of case studies, problem solving scenarios and demos that they have completed during the year. .
Sample weekly exercises topics:
|
Developing an interactive game that will allow children to learn mathematical concepts, using publicly available AI tools
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Other Assessment(s) | 100 |