This new cross-disciplinary programme will create expert data scientists taught through the exciting lens of cultural heritage.
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- Developed and delivered by leading academics at the , in collaboration with industry and
- Innovation and practice-driven MSc, giving you the opportunity to learn data science through real applied challenges that require the use of advanced skills and solutions.
- Alongside the latest data science skills, you will develop valuable cross-cutting and transferrable skills, including communication and transdisciplinary collaboration, and will develop your critical thinking.
- You will have the opportunity to work on exciting real case studies provided by leading national and international heritage organisations.
The role of digital technologies and data in all aspects of contemporary society is immediate, relevant and complex, and the multidisciplinary field of cultural heritage is no exception. From historic buildings, sites, landscapes, museums and collections, the world of heritage provides an exciting setting in which to learn and apply skills of data science.
The opportunities of using data science in diverse contexts are vast. Employers from increasingly diverse sectors now require people with skills in a range of state of the art methods and technologies to understand, manage and exploit data.
This programme pioneers a new way of teaching data science through application in a cross-disciplinary context. As a student on the Data Science for Cultural Heritage MSc (DaSCH), you will develop advanced data science skills, such as crowd sourced data science, machine learning and imaging data analysis. You will explore the complexities of acquisition, analysis and exploitation of the variety of data that is generated and used in heritage contexts, including data generated through analysis and measurement, imaging and surveying, citizen science, and digitally born data.
Data science underpins much of modern science. By examining the topic through the lens of cultural heritage, we are able to ensure the human side of data science is emphasised. The main focus of the MSc to develop your experience of applying data science methods, but linking this to heritage means that students develop a broader experience and are able to consider the needs of users, the public and a broad range of stakeholders as well as well as the more technical aspects of data science.
- Learning outcomes
Cultural Heritage presents complex challenges that bring together science, engineering and technology with social science and humanities. You will develop and learn how to apply your data skills and knowledge within the cultural heritage context, giving you a balance of academic skills, active learning experience, business capabilities and cross-disciplinary skills that are in high demand in many industries and sectors.
You will learn about:
- The data pipeline from acquisition, through exploitation, to storage and reuse
- Data analysis, visualisation and management skills
- Practical skills of data acquisition and treatment in the context of cultural heritage, such as machine learning, Heritage BIM, crowd-sourced data, AI or imaging technologies
- Legal concepts related to data management and ethics
- Data archives and repositories in the context of cultural heritage, their value and conservation
- Working with stakeholders (including the public) to acquire, contextualise and evaluate data
Furthermore, this programme will develop your critical thinking skills through the exploration of the concept of cultural heritage and the motives and needs for its preservation, as well as the importance of heritage values, integrity and authenticity.
Structure and modules
Overall, you will study four compulsory modules and three optional modules. In term one you will take three compulsory modules and in term two you will take one compulsory module complimented by three optional modules of your choice. The programme is structured so that over the course of your studies you will gain an appreciation of the cultural heritage context, whilst gaining skills in data science, applied data science and research. Learn more about the programme's structure and modules below.
- Programme structure
Cultural heritage data offers a unique and complex perspective through the data pipeline of:
Acquisition > Analysis > Visualization > Storage > Repurposing > Access > Curation
Modules in term one will introduce heritage, sustainability, and applications of science through two introductory modules on sustainability, heritage, and heritage science. In the first term you will also develop analysis and visualisation skills through modules on statistical methods and data mapping and visualisation.. In term two, the focus will be acquisition and analysis with data-specific skills taught through three optional modules of your choice as well as best-practice of data management. The third and final term is for you to hone your research skills through your data science dissertation - an independent piece of research relating to the main themes of the degree.
You will address qualitative and quantitative data generated about culture and heritage and data as heritage. Data about culture and heritage covers data generated through documentation, measurement, imaging and surveying, analysis, citizen science, as well as digitally born data, e.g. through social media and other digital social interaction, which is increasingly collected by cultural and heritage institutions. Data as heritage comprises digitally born heritage, as well as digital collections or collections of digitized heritage assets.
The MSc DaSCH is structured along four learning streams:
1) Cultural heritage context. This stream will provide you with a cross-disciplinary understanding of the cultural heritage context, its significance and value in society and will develop your critical analytical skills.
2) Core data science skills is designed to develop core data science skills required for data analysis, interpretation, visualization and management. You will learn about mathematical methods for statistical analysis, signal processing and optimisation, you will explore the principles of human-computer interaction and the basis for data organisation and data exploration, and you will develop your programming skills.
3) Applied data science skills. Through hands-on applied modules, you will be able to learn about applied data skills of great potential, not only in the heritage sector but also in other science and engineering contexts, and even social science contexts. Examples are crowd-sourced and citizen science, imaging data analysis, Heritage Building Information Modelling and many more.
4) Research skills. During the programme, you will explore the latest cutting-edge research methods and technologies, enabling you to develop your own research skills, which will help you through your dissertation work.
- Introduction to Sustainable Heritage (Term one)
This introductory module, shared across all of the Master’s programmes in the UCL Institute for Sustainable Heritage, offers students the chance to engage with the fundamental concepts and principles that underpin all the work undertaken by the Institute. It presents an opportunity for students to learn next to peers following different programmes within ISH, illustrating the range of disciplines that contribute to the fields of sustainable heritage and heritage science.
The module has three fundamental aims:
- To introduce students to the shared concepts and principles that are fundamental to the field of sustainable heritage.
- To demonstrate to students the necessarily interdisciplinary nature of sustainable heritage, illustrating how their chosen speciality complements and builds on the work of those in other programmes.
- It aims further to develop students’ sense of themselves as researchers, and to equip them with the skills necessary to work across academic disciplines.
Students will consider what is and what is not heritage, the shape and nature of the heritage sector, how and in what ways heritage is valued, the role played by heritage in developing resilient and sustainable societies, how scientific data and evidence is used within heritage, and heritage risks and possible futures.
The module will illustrate the social and material systems that form heritage, and develop students’ understanding of different research paradigms and methodologies, equipping them to collaborate in a truly interdisciplinary way.
- Introduction to Heritage Science (Term one)
This module provides an introduction to the variety of aspects of heritage science, ranging from exploring the concept and value of heritage, to the role that science, engineering and digital technologies can have in in this multi-disciplinary field. You will acquire new skills in a highly pragmatic context, incorporating team working, problem-based teaching, case-based learning, discussions, presentations and reports. At the end of the module, you will be able to:
- use the framework of heritage values to interpret scientific results;
- understand, interpret and document typologies of art, heritage and archaeology;
- recognise value typologies;
- understand digitisation and preservation of digitally born information appreciate aspects of material and environmental change;
- understand issues related to prevention, intervention and risk management;
- strategically evaluate the impact that heritage research can have on wider societal and environmental issues;
- review the state–of–the–art in the intersection of heritage and data science.
- Heritage Data Mapping and Visualisation (Term one)
Structuring and transforming data so that it provides meaningful information to heritage managers or the general public often requires skills to visually interpret, display and map data. Transformation of raw information requires an understanding of how data translates into heritage information, whether to reveal archaeological and architectural structures or art shapes and patterns. This requires an understanding of the specific material or engineering processes that are used to interpret data. Specific examples will include 2D and 3D visualization and analysis of data, e.g. using mesh files, but also including display of geo-localised data and non-geometric data in visually meaning ways. The module will culminate in a practical exercise to synthesise 2D, 3D, historical, and scientific information into a visualisation relevant to a heritage context.
- Heritage Data Management (Term two)
This module will cover the specific issues related to management of heritage data, such as curatorial, conservation or reuse, as well as legal and ethical. Technical content will involve developing the skills and knowledge needed to structure data in order to enable exploitation and reuse, and challenges related to long-term storage and particularly preservation of large datasets. The principles of findability, accessibility, interoperability, migration and reusability will be explored in detail, as they define the future value and particularly the economic value of data collected or required by heritage institutions.
Students will complete one of two statistics modules, depending on their existing statistical experience and interest. A self-assessment at the beginning of the year is used to determine which is most applicable for each student.
- Introduction to Statistical Data Science (Term one)
This course, taught at UCL Department of Statistics, covers essential probability and statistics required for applied multidisciplinary data analysis. The topics include:
- Exploratory data analysis
- Basic probability models
- Point estimation, maximum likelihood and basic optimization
- Hypothesis testing
- Confidence intervals
- Dimensionality reduction
- Statistics for Heritage Science (Term one)
This module introduces fundamental statistics concepts. It is taught using examples and case studies relevant to cultural heritage, but it provides data analysis skills that are transferable to any other field. The module welcomes students with any background. By design, it can be followed with little pre-existing knowledge of statistics.
The module prepares students for the further study of statistical data science. It begins with a review of data typologies and finishes with multivariate regression. In between, students will learn probability, statistical distributions, the fundamentals of inference and linear regression. The module also helps students develop two skills: programming and linear algebra. It is taught using the statistical package R, with exercises that increase gradually in complexity. It also includes introductory lectures on mathematical notation and algebra.
- Technologies and digital approaches for built heritage
This module will explore the challenges and opportunities of employing technologies and digital approaches within the historic built environment to inform management and decision making. Students will gain skills to tackle heritage-specific issues using approaches such as BIM software, non-destructive testing equipment, and other approaches to managing information about built heritage.
The key learning outcomes for this module are:
- Learn about a range of technologies and digital approaches relevant to built heritage
- Develop data acquisition, processing, and management skills
- Understand how technologies and digital approaches are part of the building information modelling process
- Creatively apply technologies and digital approaches to built heritage in small-scale group projects
- Heritage Imaging
In this module you will develop an understanding of the use of imaging methods in heritage characterization and visualization, and the subsequent image analysis. We aim to cover a broad range of imaging techniques, including scientific photography, multispectral imaging, hyperspectral imaging and x-ray imaging. The information obtained typically requires image data analysis using either proprietary or open source software, which you will learn and use to analyse their datasets. Where possible, the Institute for Sustainable Heritage Imaging Laboratory (at HereEast) will be used for practical teaching, and the case studies will be based on existing research at ISH as well as recent EPSRC investments into robotically enabled hyperspectral imaging equipment. The module will be assessed though the submission of a 2500 word report explaining the acquisition and/or analysis of a real image data set.
- Crowd-Sourced and Citizen Data for Cultural Heritage
Crowd-sourcing is not only transforming the way heritage data is obtained but also how it is managed, researched and disseminated. By enabling anonymous citizens or visitors to produce measurements or carry out small tasks, heritage institutions can simultaneously increase data collection rates and engagement levels. These methods are, however, subject to important practical and theoretical problems. This module equips students with the tools to think critically about crowd-sourcing methods in heritage and to evaluate the data obtained by them. Students will learn how to design a crowd-sourcing exercise, and experiment with appropriate measures to control the quality of the obtained data. The data-gathering methods may be: the use of smartphones to obtain visual information (colour, dimension, area, shape), wearable and low-cost sensors to obtain information that can inform preventive conservation (pollutants, moisture, temperature) or to obtain textual information that can help interpretation (tags, classification of images, transcriptions). Students will also develop statistical skills to assess the metrology of the crowd-sourced data. Finally, they will explore the scientific and ethical issues associated with this new type of analytical technique.
- Machine Learning for Heritage
Machine Learning is one of the most prominent and effective ways of turning data into products. The applications to the heritage sector are innumerable, ranging from heritage recognition and identification, to managing archives and repositories. This module will provide a gradual introduction to the fundamental principles of Machine Learning. The module has two main components: practical skills and advanced case studies. In the first component, students will learn the basic maths and coding skills required to create a neural network. This aspect of the module emphasizes an understanding of algebra, matrix operations and programming. In parallel to this, experts that have used machine learning in heritage will present case studies that illustrate a diversity of strategies. These sessions will cover transfer learning, decision trees, convolutional neural networks and other algorithms. The assignment is an open-ended exercise, where students will use one of the methods learned in the module to analyse a dataset.
- Environmental-Material Interactions
The analysis of environmental data and its relationship with material degradation is one of the central aspects of heritage conservation. This module aims to equip students with data analysis skills, a basic operational knowledge of the most important experimental techniques and an understanding of the relevant physical processes.
Students will learn the material typologies of heritage objects and collections, and the fundamental processes involved in their degradation. Through the involvement of industry experts, they will learn how change and damage are monitored and prevented.
Some lectures will take place in the Heritage Science Laboratory, where students will learn how to operate analytical instruments used to evaluate material deterioration. This will include microfadometry, viscometry, near-infrared spectroscopy and related techniques.
In problem classes, the students will learn methods of analysis for environmental data, studying how temperature, humidity, light and pollution raw data can be transformed into actionable information for material conservation through standards and guidelines, isoperms, isopleths, metal corrosion indexes and analysis of fluctuations.
Students following the MSc Data Science for Cultural Heritage are required to submit a 10,000-word dissertation. The topic of the dissertation, which is supervised by a member of BSEER staff, is selected by the student in agreement with the programme director. It can be taken from a wide range of subjects related to the main themes of the programme and may be selected to assist career development or because of its inherent interest. Collaboration with industry for the selection of dissertation projects will be encouraged and facilitated whenever possible.
The programme can be studied full-time over one year, two years part-time, or two-to-five years flexibly.
Tuition fee information can be found on the UCL Graduate Prospectus.
For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the section of the UCL website.
DaSCH is taught at UCL’s Bloomsbury Campus and UCL at HereEast (Stratford, east London).
The Institute for Sustainable Heritage has collaborated with many of the world’s leading organisations in the field, including the V&A, Tate, the British Library, the National Archives, the Smithsonian Institution and UNESCO, as well as many industry partners. Guest speakers drawn from the Institute’s extensive contacts across the heritage sector will be invited to lead specialist lectures and tutorials, providing specialist knowledge as well as industry practice.
The MSc DaSCH is supported an Advisory Board of industry experts who contribute to the course development. The Advisory Board is composed of:
- Paul Bryan, Geospatial Survey Manager, Building Conservation & Geospatial Survey Team, Historic England
- Fenella France, Chief, Preservation Research and Testing Division, Library of Congress
- Adam Frost, Senior Digital Documentation Officer at Historic, Historic Environment Scotland
- Barbara McGillivray, Turing Research Fellow, The Alan Turing Institute
- Maureen Pennock, Head of Digital Preservation, British Library
- Damien McCloud, Honorary Professor, UCL and Associate Director at Arup
- Sonia Ranade, Head of Digital Archiving, The National Archives
Students learning to use BIM Module laser scanner at St Pancras church
The programme draws upon the full range of expertise offered by the UCL Institute for Sustainable Heritage, the UCL Department Statistical Science, and industry and sector leaders. The DaSCH programme is delivered by some of the world's most respected experts in their disciplines, producing both substantial scholarly work and highly innovative research at the leading edge of their fields.
- Key staff
Dr Josep Grau-Bove
Professor Matija Strlic
Data science is in high demand in many and diverse industries. As a graduate of the DaSCH MSc you will be ideally placed to gain employment as a data scientist, in particular in those sectors that foster transdisciplinarity and break down barriers between technology and humanities or social sciences. The MSc will equip you to succeed as a data scientist in diverse fields such as heritage science and technical roles in cultural institutions, the built environment, digital technologies and media, data analytics, and software engineering. Moreover, you can also pursue a career in academia.
Cross- and trans-disciplinarity, critical thinking, an applied focus and an emphasis on innovation are the key qualities that will define the professional character of our graduates, and will make you stand out from other data scientists.
The programme has been developed with input from industry leaders from a diverse range of sectors, including architecture, heritage, social media and digital technologies. You will gain exposure to real data challenges from these industries and will develop a skill-set in data science that will be highly transferable across these and many other sectors.
- See detailed module information for this programme
- For more key programme information, including how to apply, please visit the UCL Graduate Prospectus
- If you haven’t found the information you need, you can email the Programme Lead, Dr Josep Grau-Bove email@example.com
- For administrative information, please contact firstname.lastname@example.org
- Guidance on writing your Personal Statement for the MSc Data Science for Cultural Heritage
In addition to the expectations set out centrally by UCL for graduate applications, we recommend considering the following when preparing your Personal Statement for this MSc programme.
- Academic interest: How can data science inform the interpretation, management, or engagement with heritage? What makes this different in a heritage context?
- Professional experience and internships: Be specific about how you contributed to a project, and what sort of timeframe it was completed in
- Career plans: a degree in data science can equip you with transferrable skills to move between many domains, thus preparing you for a wide range of careers. Equally, if you are not sure, you can discuss the kinds of opportunities that you think may be of interest.
- If there is a specific module that is of interest or relevance to you, do mention it and explain why in detail
- Conservators: include a reflection on how independent learning (e.g. online Python) connect to their experiences and have potential to influence their ways of working