Music is being transformed by data, from music making to music consumption. In an era in which data permeates every facet of our lives, it has become evident that the intersection of music and data science offers exciting possibilities. This course aims to equip you with the skills to navigate this dynamic landscape. By blending artistic and subject-specific music knowledge with data science, the programme aims to bridge the gap between creativity and data-driven insights.
This innovative interdisciplinary course combines advanced study in both music and computer science and is aimed at those wishing to develop a unique skillset that combines music knowledge with data science expertise. You'll also benefit from engagement with the School of Music, the School of Computer Science and the Business School, providing valuable experience of different disciplinary approaches. The MA Music and Data Science draws on the expertise and resources of the School of Computer Science, the School of Music and Leeds University Business School. Studies in data science provide a strong foundation in the core topics of data mining, machine learning, and data analytics. This ensures that you’ll benefit from training in data science techniques and programming skills.
Studies in Music offer a range of modules covering music data history and the music industries including topics such as the recording industry, music publishing and digital marketing. The course culminates in the 60-credit Music and Data Science Project. Through this major project, you’ll get chance to apply your data science skills to real-world challenges in the music industry, bridging the gap between technical knowledge and practical application.
[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable
Candidates will be required to study the following compulsory modules:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| COMP5122M | Data Science | 15 | Semester 1 (Sep to Jan) | |
| COMP5712M | Programming for Data Science | 15 | Semester 1 (Sep to Jan) | |
| COMP5840M | Data Mining and Text Analytics | 15 | Semester 2 (Jan to Jun) | |
| LUBS5990M | Machine Learning in Practice | 15 | Semester 2 (Jan to Jun) | |
| MUS5011M | Music and Data Science Project | 60 | 1 Jan to 30 Sep | PFP |
| MUS5113M | Music Data Research | 30 | Semester 1 (Sep to Jan) |
Candidates will be required to study 30 credits from the following optional modules:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| MUS5112M | The Recording Industry Now | 30 | Semester 1 (Sep to Jan) | |
| MUS5211M | How Songs Make Money | 30 | Semester 2 (Jan to Jun) | |
| MUS5331M | Short Dissertation | 30 | Semesters 1 & 2 (Sep to Jun) | |
| MUS5332M | Individual Project | 30 | Semesters 1 & 2 (Sep to Jun) |
Please note that optional modules run subject to enrolments. An optional module may not run if only a low number of students choose it.
Last updated: 30/04/2026 16:03:26
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