136011 UE Data Structures and Data Modelling (2026S)

This course introduces students to the basic structure of digital data and to principles of data modelling based on requirements analysis, with a particular focus on humanities data. It presents and compares the main data modelling paradigms used in the field: the relational data model (implemented using SQL), hierarchical data models (implemented using XML and JSON), and the graph data model (implemented using Neo4j). Students will acquire both conceptual and technical knowledge to understand how different modelling choices affect the representation, management, and reuse of data in humanities research.

The course has a strong practical orientation. Learning takes place through in-class exercises, collective discussion and correction of assignments, and the guided development of a final group project. The final project consists of a working data model and written documentation explaining the design decisions and technical implementation. Both in-class sessions and independent work outside class will be dedicated to the development of this project. Working with real-world datasets, students will analyse project requirements, design appropriate data structures and data models, and implement them technically. Throughout the semester, student groups will present their requirements analysis, discuss proposed modelling solutions, and progressively develop and refine their implementations, with feedback provided during class.

Practical work will be complemented by discussions of empirical and theoretical frameworks related to data processing and data mining, with particular attention to humanities research questions.

No prior programming knowledge is required; all necessary technical skills will be introduced during the course.