Computational Musicology: Quantitative Approaches to Music History

Music history is no longer confined to manual score analysis and library archives. By applying modern quantitative methods, you can uncover hidden patterns, stylistic evolutions, and structural trends across thousands of musical pieces at once. This course introduces you to the field of computational musicology, showing you how to bridge the gap between music theory and data science. You will explore how musical scores are digitized, encoded, and analyzed using computational tools to answer compelling historical questions. Through clear explanations and practical code walk-throughs, you will learn how to turn musical compositions into analyzable data. What you'll learn: - Understand the foundational concepts of computational musicology and digital music representation. - Explore digital music encoding formats including MIDI, MusicXML, and metadata schemas. - Analyze musical structures, harmonies, and intervals using Python-based music analysis libraries. - Perform corpus studies to track stylistic shifts across different historical eras. - Apply basic data visualization techniques to represent musical features and patterns. - Evaluate the challenges and limitations of quantitative data in historical music research. The course begins with core definitions of digital musicology and music encoding before moving into structured, text-based guides on parsing, analyzing, and interpreting musical datasets. This course is designed for musicians, historians, and data enthusiasts eager to merge music with technology. No advanced programming or deep music theory background is required to begin. Start exploring music history through a quantitative lens today.