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Honoursmodule: The Data Science of Everyday Music Listening


John Ashley Burgoyne

Entry requirements 

For 2nd or 3rd year honours students only. 

Recommended prior knowledge

A basic knowledge of music theory (e.g., keys, scales, intervals, and chords) and some experience with data visualization is helpful but not necessary. Previous experience with R, data visualisation, and data science is helpful but also not required: the course meetings and materials will be sufficient for even technical novices to make a start. The only true requirement beyond the standard requirements for the honours programme is a passion for music and a desire to learn more about why it is so important to us.

In the group work and research portfolios, students are strongly encouraged to make explicit use of skills and preliminary research studies they developed in other courses and to share that background with their group. Part of the excitement of an honours module is the synergy that can develop when students from very different quarters of the university come together to work on a shared problem.

Learning Objectives

  • recognise common quantitative measurements used in music research,
  • compare musical audio collections quantitatively using standard features from music information retrieval,
  • measure musical behaviour using verified instruments from music cognition,
  • discriminate among audio collections and listening behaviours on the basis of simple statistical models,
  • design survey-based studies of music listening in everyday life,
  • judge the quality of quantitative musical research and provide constructive criticisms for future work.


The average person today spends as many hours listening to music as the top 10% of all music listeners a decade ago – and this average rate of music listening is increasing more than 15% per year. Due to the rise of relatively low-cost streaming services and the ubiquity of portable music devices and smartphones, the average person is also listening to music in a wider variety of context than ever before. ‘Everyday music listening’ has grown into a research field in its own right, encompassing perspectives from the humanities (e.g., How do youth use music to establish a robust identity and sense of self?), the exact sciences (e.g., What is the most effective algorithm for making personalised music recommendations like Spotify’s Discover Weekly?), and the social sciences (e.g., To what extent does personality determine musical taste?).

Working in small groups, students in this course will learn how to synthesise heterogenous sources of quantitative data to understand more about their own daily musical experiences and the musical experiences of others. The course takes a breadth-first approach so that (1) students will be exposed to as many areas as possible where they can apply skills they have developed in other courses to music and (2) students with a strong interest in the subject will be prepared to deepen their understanding in courses such as Computational Musicology or Cognitive Musicology. Each group will design, execute, and analyse a small research experiment on everyday music listening, incorporating questionnaires from the music cognition literature and listening histories from streaming services like Spotify and Apple Music, guided and inspired by a serious of lectures and practical sessions on research topics and tools from cognitive and computational musicology.

The first half of the course will focus designing a good research experiment, including practical sessions about tools like the Spotify Developer API and online surveys with Qualtrics. Students will also be exposed to major trends in quantitative musicological research and will be asked to write a short article review on a topic of their choice. This portion of the course will conclude with group presentations of their research plans.

In the second half of the course, as each group is collecting data, the course content will shift its focus toward analysis and visualisation techniques. Over the course the second half, students will build up an online portfolio in place of a research report to document their experiments and results. In addition to working on their own portfolios, students will make weekly peer reviews of work from other groups, so that the best ideas can spread easily across the course. After final group presentations of the research results in the last week of the course, each student will choose a research project about which to write an extended critique, with an emphasis on potential directions for further study.

The planned sequence of course meetings is as follows, although the course coordinator may deviate from these topics according to the needs of the group.

  1. Everyday Music Listening
  2. Music Information Retrieval and Music Cognition
  3. Practicum: The Spotify API
  4. Survey Design and ‘Musical Instruments’
  5. Practicum: Music Research with Qualtrics
  6. Special Topic: The Eurovision Song Contest
  7. Mid-Term Presentations: Research Plan
  8. Practicum: Data Dashboards and Visualisation
  9. Practicum: Data Wrangling with R
  10. Practicum: Classical and Non-Classical Analytical Techniques
  11. Common Confounders in Music Research
  1. Final Presentations: Research Portfolios

Class contents

  • Lecture
  •  Seminar
  •  Laptop college
  •  (Computer)practicum
  •  Presentation / symposium
  •  Selfstudy
  •  Work independently on project/ thesis


  • MIR/music cognition article review (15%; individual)
  • Research-design presentation (25%; group)
  • Final research portfolio (25%; group)
  • Peer reviews (10%; individual)
  • Portfolio critique (25%; individual)

Min/max participants

max. 25


Check Datanose for the exact information.

Study material

  • Literature > weekly readings available on Canvas
  • Syllabus > available on Canvas
  • Software > Spotify, R/RStudio, Git/Github, and Qualtrics


Registration is possible for 2nd year (or higher) students participating in an Honours programme. The registration for the Honours courses will start on June 1, 10 am -  June 4, 11 pm, You can register through the online registration form that will appear on Honoursmodules IIS (registration is NOT through SIS)

Placement will be at random and students will be informered about their placement in the week of June 21. 

There is NO guarantee for placement if you register AFTER June 4, so make sure you apply on time! 

For questions about registration please email to: Honours-iis@uva.nl 



All of the software used in the course is free of charge for students. Although a Spotify Premium account is substantially more pleasant to use for daily listening, all of the analyses necessary for this course are possible with a free account.

Students are required to bring a laptop to all practical sessions and will probably find a laptop helpful at all sessions.


SDGs in education

The IIS strives to reflect current societal issues and challenges in our elective courses, honours modules and degree programmes, and attempts to integrate the following Sustainable Development Goals (SDGs) in this course. For more information about these goals, please visit the SDGs website


Facts & Figures
Mode Honours programme
Credits 6 ECTS,
Language of instruction English
Conditions for admission
Starts in November