Module manager: Dr Aditya Lal
Email: A.Lal@leeds.ac.uk
Taught: 1 Jan to 30 Sep View Timetable
Year running 2026/27
This module is not approved as an Elective
This capstone module enables you to integrate technical data science skills with music industry knowledge through a substantial independent project. You will design and execute a data-driven research project on a music-related topic, demonstrating both computational competence and music-sector insight. You could choose to analyse streaming behaviour, artist development strategies, audience intelligence, market trends, industry business models, or any other relevant aspect of the contemporary music industries that may be productively explored through a data-science lens. You will develop resourcefulness in sourcing datasets from public APIs, industry partnerships, or your own professional networks, conduct analysis, and produce critically evaluated, data-driven outputs that may include analytical reports, visualizations, or software implementations.
This module aims to develop your capacity to independently design, execute, and communicate a substantial data-science project relating to music. Through supervised independent research, you will formulate research questions, select appropriate methodologies, acquire and analyse relevant datasets, and produce outputs to a high professional standard. The module also functions as a flexible space within which you can synthesise learning from other modules completed on your programme and develop your professional autonomy and the critical self-assessment capabilities essential for data science practice.
On successful completion of the module students will be able to:
1. Design and execute a project in music and data science that synthesises and applies knowledge and methods from across the programme to address a substantial, self-identified problem.
2. Source and manage datasets from diverse sources working within relevant commercial and ethical requirements.
3. Select and apply appropriate analytical, computational and other methodologies used in data science for music-related datasets.
4. Critically evaluate datasets, analytical approaches and findings
5. Communicate and present data science work effectively to diverse technical and non-technical audiences through appropriate formats
6. Apply professional standards and appropriate digital tools, analytical software and technical resources to produce high-quality outputs
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Supervision | 12 | 0.5 | 6 |
| Lecture | 4 | 1 | 4 |
| Seminar | 6 | 1 | 6 |
| Private study hours | 584 | ||
| Total Contact hours | 16 | ||
| Total hours (100hr per 10 credits) | 600 | ||
Students will receive formative feedback through supervisory meetings to support their development of project plans, methodological approaches, and solutions to technical challenges.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Investigative Project | Investigative Project | 100 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Check the module area in Minerva for your reading list
Last updated: 01/04/2026
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