Digital Art History – online 2025 and 2026 entry

For more details, including weekly contact hours, teaching methods and assessment, please see the module catalogue. The modules are examples from previous academic years and may be subject to change before you start your course.

• MLitt

  Dissertation or final project

  Following the successful completion of the coursework (see module details under PGCert and PGDip below), MLitt students undertake a dissertation module of 60 credits (15,000 words). The dissertation should demonstrate an element of independent thinking or originality of thought in the establishment of the argument and be based upon clearly founded research, analysis and interpretation. It should demonstrate the organisational and presentation qualities required for academic work.

  Students can expect to meet with their supervisors at least three times during the research and writing of their dissertations and to make appropriate arrangements for the submission of drafts and timely feedback.

• PGCert and PGDip

  • Introduction to Digital Art History and the Humanities: introduces the key issues, themes, and ideas relating to Digital Art History.
  • Programming in Python: introduces and revises modelling, design and implementation in Python.
  • Digital Art – From Computer-generated Projects to Artificial Intelligence: analyses the ways in which artists, archivists, curators, and other museum professionals have implemented digital tools, materials and methodologies.
  • Digital Tools – Visualisation, Interpretation and Analysis: introduces the key techniques and digital platforms available to art historians and the ways in which they can facilitate (or limit) our analysis, interpretation, and visualisation of art historical data.
  • Project Work – Theory: will challenge students to critically analyse the methodological choices made by digital art historians. A series of major research projects will serve as case studies, allowing students to explore how the theories and approaches central to the disciplines of Digital Humanities and Art History work in practice.
  • Project Work – Data Analysis: requires students to design and develop their own research project based on a given data set drawn from real collections.
  • Data and information visualisation: focuses on the question of how to utilise visual representations to make information accessible for exploration and analysis.
  • Machine learning algorithms: covers the essential theory and algorithms, including mathematical foundations, and methodological approaches, using a variety of regression, classification and unsupervised approaches.
  • Complex systems modelling and simulation: introduces a range of techniques and their applications to different classes of problems, with a practical focus on modern network-based models and simulation.
  • Discrete optimisation: covers the theory, tools and technologies developed and used to solve problems in Integer Programming and Combinatorial Optimization.
  • Data-driven systems: is an advanced research-focused module that presents the foundations of distributed systems and techniques that process data.
  • Numeric optimisation: takes linear algebra and optimization as the primary topics of interest and solutions to machine learning problems as the applications of the resulting tools, techniques and algorithms.
  • Research methods in data science: introduces the skills necessary for the planning, data gathering, data analysis and dissemination stages of data science research.
  • Knowledge discovery and datamining: covers many of the methods found under the banner of ‘datamining’, building from a theoretical perspective but ultimately teaching practical application.