Module manager: TBC
Email: TBC
Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2026/27
Data Science at the level of MATH1603, MATH1604, MATH2603, MATH2604
N/A
This module is not approved as a discovery module
Students will conduct an independent research project in a topic in data science, possibly with local or industrial partners. They will receive training in research skills and develop and implement a personal training plan. Students will meet in groups to discuss the project topic, with each student researching a specific aspect of the topic, with a view to knowledge transfer. Students will produce an individual project output and may present their work orally and in writing individually or collaboratively.
This module’s aims are to - complete a significant piece of independent work, with appropriate guidance, support and resources, informed by all other modules in order to put into practice the data science lifecycle with an authentic data project. - combine technical analysis and a design approach with an awareness and appreciation of the wider context. - lay the foundations for work in a professional setting, thinking about both technical aspects and the wider organisational or societal context. - use targeted & tailored training, coaching and feedback, and design and collaborative approaches. - communicate results in technical report and presentation form. - reflect on professional skills, how to evidence attainment, and to identify strategic development goals. - serve as a capstone project for someone with a critical, scientific mindset who is also a reflective, strategic and lifelong learner. They will develop their research practice and gain experience in applying different tools. By completing several milestones, they get practice in communicating their findings in different forms.
On successful completion of the module students will have demonstrated the following learning outcomes: 1. Integrate interdisciplinary skills from algebra, statistics, modelling, computer science, machine learning, data handling, behavioural sciences and domain knowledge for the successful execution of a data science project such as in sustainability, community engagement, industry practice or academic contexts. 2. Think critically and creatively from data literacy, strategic and ethical perspectives. 3. Critically evaluate inferential arguments from different sources, perform rigorous, robust and reproducible mathematical and computational analyses, and communicate findings using appropriate approaches for different audiences.
1. Introduction of project topics with detailed project briefs in diverse areas such as theoretical aspects of data science, finance, healthcare, government, sustainability, energy, community partners etc. 2. Research skills training: includes finding and evaluating scientific resources, referencing and ethics. 3. Professional skills coaching: knowledge transfer, presentations, organisational behaviour and decision makers. Feedback and reflection. 4. Personal development plan: reflecting on strengths and weaknesses, and implementing a plan to address gaps in graduate skills. Reflection on professional competency frameworks and job specifications, demonstration via a portfolio. 5. Reading groups or community partners: students will engage in directed preparation in the broad area of their topic, possibly involving external stakeholders. 6. Independent research: students will carry out research individually and collaboratively.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Supervision | 11 | 0.5 | 5.5 |
| Lecture | 12 | 1 | 12 |
| Private study hours | 382.5 | ||
| Total Contact hours | 17.5 | ||
| Total hours (100hr per 10 credits) | 400 | ||
Students will receive regular feedback from supervisors in supervision meetings, and for specified milestones. Students will receive formative feedback and coaching on their presentation skills in order to foster this critical industry skill.
Check the module area in Minerva for your reading list
Last updated: 12/05/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team