University of St Andrews - Online

MLitt / PGDip / PGCert in Digital Art History - Online

Those studying for an MLitt take five modules in Art History, three modules in Computer Science and complete a dissertation or final project in an agreed-upon subject.

Those studying for a PGCert take three modules in Art History and one module in Computer Science. Those studying for a PGDip take five modules in Art History and three modules in Computer Science.

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 Optimisation.

Data-driven systems

It is an advanced research-focused module that presents the foundations of distributed systems and techniques that process data.

Numeric optimisation

Takes linear algebra and optimisation 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 data mining

Covers many of the methods found under the banner of ‘data mining’, building from a theoretical perspective but ultimately teaching practical application.

You will learn:

• The possibilities, limitations, and risks of digital technologies and their impact on the arts and industry.
• How digital tools are changing the way people engage with and understand collections and artefacts.
• Issues of ethics, inclusivity, and accessibility – is digital art history truly more equitable and accessible?
• Digital access, creative working and decision-making about collections covering themes such as the role of AI and using technology to decolonise collections.

You will develop and explore your own research questions to test your new computational skills in action, using the techniques needed to succeed in an increasingly digital world, including:

• Data and text mining
• The principles of visualisation, design, and interaction
• Practical visualisation techniques and tools
• Digital mapping techniques
• Machine learning

This course is designed to give you the knowledge and skills needed to make a success of your career, whether you are looking to grow in an existing role or are new to the sector.

Our Art History graduates work in a variety of roles, including universities, museums, galleries, auction houses, media, and art dealerships, in roles including research, curation, collections, archives, exhibitions, and learning and engagement.

Salaries in heritage tend to be higher with a postgraduate qualification. Government research finds that taught postgraduates have a lifetime salary premium of £61,000 compared with graduates with an undergraduate degree.

Wherever you are, you can take St Andrews with you. Online Master's at the University of St Andrews combine all the benefits of studying at one of the world's oldest and best universities, with all the advantages of flexible, personalised learning.

Whether you join us online or in person, when you graduate, you become a member of the University's worldwide alumni community. Benefit from access to alumni clubs, the Saint Connect networking and mentoring platform, and career support.

Teaching

Delivered through lectures, tutorials and practical work.

Schedule

You will access modules and components at a pace and on a timetable that suits your work and study environment.