Module manager: TBC
Email: TBC
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2026/27
Data Science at the level of MATH1603, MATH1604, MATH2603, MATH2604
| MATH2603 | Graphs, Networks and Systems |
| MATH2604 | Machine Learning and Object-Oriented Programming |
N/A
This module is not approved as a discovery module
This module develops more advanced topics in AI and data mining in professional industrial contexts such as finance, health, energy, sustainability etc. Key topics are Deep Learning and Explainable AI, for instance in the context of NLP, unstructured data labelling, object recognition and ethics. It reflects on the data science lifecycle in an organisational strategic context with a view to communicating insights and recommendations to institutional decision makers, achieving organisational goals, or effecting change.
The key objectives of this module are to - foster the ability to perform AI tasks and to critically assess their significance in an organisational context. - reiterate the nature of data science, the strategic context of data projects, and the organisational role of data scientists as communicative and collaborative generalists. - develop key competencies in advanced contemporary data science techniques, building on advanced algebra, statistics and computation modules. - develop the ability to apply these techniques in a strategic context of organisational behaviour, business intelligence and strategic goals, with a view to communicating findings to decision makers, making convincing and persuasive arguments for recommendations, effecting change and achieving organisational goals.
On successful completion of the module students will have demonstrated the following learning outcomes: 1. Select, explain and apply key Deep Learning techniques to real-world applications, such as searching, natural language processing, computer vision or object recognition. 2. Select appropriate tools for explainable AI and deconstruct the ethical arguments surrounding causal explanations and singularity. 3. Apply and integrate technical, behavioural and domain knowledge in order to address multi-disciplinary real-work challenges. 4. Explain, employ and evaluate advanced AI tools and techniques from an organisational strategic perspective.
1. Deep Learning paradigms such as convolutional, recurrent, graph neural networks, autoencoders, generative adversarial networks, transformer models etc; implementation using appropriate libraries and environments; performance and evaluation; applications such as object recognition, unstructured data, labelling, computer vision, causal models, data mining, text mining, word2vec, natural language processing, large language models, multiple input modalities, genetic algorithms, Bayesian networks, game play etc 2. Explainable AI: causal explanations, tools such as gradient saliency, SHAP etc 3. Ethics of AI: soft/hard/general AI and singularity, explainability, sustainability etc 4. Ensemble methods: bagging, boosting, model averaging; smoothing and aggregating, e.g. kernel methods 5. AI and data science projects in an organisational context: knowledge transfer, data storytelling, strategic goals, influencing stakeholders, data visualisation, communicating recommendations to decision makers, business context, effecting change, impactful data science projects, business intelligence; dashboards, executive presentations, coaching and reflection
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Practicals | 22 | 2 | 44 |
| Independent online learning hours | 26 | ||
| Private study hours | 130 | ||
| Total Contact hours | 44 | ||
| Total hours (100hr per 10 credits) | 200 | ||
Formative learning opportunities in the studio-style class as well as formative opportunities to practise the skills that will be summatively assessed.
Check the module area in Minerva for your reading list
Last updated: 12/05/2026
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