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The Data Science for Cultural Heritage MSc (DSCH) provides an innovative opportunity to study data science through the exciting lens of cultural heritage. It is the first MSc to provide in-depth, practice-based data science training in a cultural heritage context, and aims to broaden the horizons of data science. The MSc will equip you to succeed as data scientist in diverse fields such as marketing, architecture, construction or media, as well as heritage and many more.
Key information
Programme starts
September 2020
Modes and duration
Full-time students study for 37.5 hours per week during term time. Typically, lectures and seminars occur on two days per week. Part-time and flexible mode students normally attend half this amount.
Application dates
Tuition fees (2020/21)
Note on fees:
The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website. Fees for flexible, modular study are charged pro-rata to the appropriate full-time Master's fee taken in an academic session.
Entry requirements
A minimum of a second-class UK Bachelor's degree from a UK university or an overseas qualification of an equivalent standard is required.
English language requirements
If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency.
The English language level for this programme is: Standard
Further information can be found on our English language requirements page.
International students
Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.
International applicants can find out the equivalent qualification for their country by selecting from the list below.
Select your country:
About this degree
This programme pioneers a new way of teaching data science through application in a cross-disciplinary context. You will explore the complexities of acquisition, analysis and exploitation of the variety of data that is generated and used in heritage contexts. You will develop advanced data science skills, such as crowd sourced data science, machine learning or imaging data analysis.
Students undertake modules to the value of 180 credits, comprising 120 taught credits and a 60 credit dissertation.
The programme consists of four compulsory modules (75 credits), three optional modules (45 credits) and an individual research dissertation (60 credits).
A Postgraduate Diploma of four compulsory modules (75 credits) and three optional modules (45 credits) is offered.
Upon successful completion of 180 credits, you will be awarded a MSc in Data Science for Cultural Heritage. Upon successful completion of 120 credits, you will be awarded a PG Dip in Data Science for Cultural Heritage.
Compulsory modules
Students will take three compulsory modules in the first term and a fourth one in the second term.
- Science and Engineering in Art, Heritage and Archaeology in Context (30 credits)
- Introduction to Statistical Data Science (15 credits)
- Heritage Data Mapping and Visualization (15 credits)
- Heritage Data Management (15 credits)
Optional modules
- Machine Learning for Heritage (15 credits)
- Heritage Imaging (15 credits)
- Crowd-sourced and Citizen Data for Cultural Heritage (15 credits)
- Heritage Building Information Modelling (15 credits)
Please note that the list of modules given here is indicative. This information is published a long time in advance of enrolment and module content and availability is subject to change.
Dissertation/report
Students are required to submit a 10,000-word dissertation (60 credits). The topic of the supervised dissertation 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 or the heritage sector for the selection of dissertation projects will be encouraged and facilitated whenever possible.
Teaching and learning
The programme is taught using various strategies including lectures, tutorials, problem-based learning, project work, coursework and reports.
You will get hands-on experience working with realistic data-sets and within heritage contexts, which will include field trips.
Skills-based learning will be delivered through small-group exercises promoting peer-to-peer learning and learning through research.
Additional costs
You will require your own laptop. Recommended specifications can be provided on request.
Accessibility
Details of the accessibility of UCL buildings can be obtained from AccessAble. Further information can also be obtained from the UCL Student Support & Wellbeing team.
Funding
For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.
Careers
Data science is in high demand in many and diverse industries. As a graduate of MSc DSCH you will be ideally placed to gain employment as data scientist, in particular in those sectors that foster interdisciplinarity and break barriers between technology and humanities or social sciences.
The programme has been developed with input from industry leaders from a diversity of sectors, including architecture, heritage, social media or digital technologies. You will gain exposure to real data challenges from these industries and will develop skill set in data science that will be highly transferable across these and many other sectors.
Employability
Cross-disciplinarity, an applied focus, an emphasis on innovation and critical thinking are the key qualities that will define the professional character of our graduates and will make you stand out from other data scientists.
You will develop advanced data science skills, as well as many transferrable skills such as coding, presentation and communication skills, working with different stakeholders, problem contextualization or public engagement techniques.
Why study this degree at UCL?
From historic buildings and sites to museums, cultural heritage provides an exciting setting to learn and apply data science through real applications that combine science and engineering with social sciences and humanities.
This cross-disciplinary programme will give you a balance of advanced data science skills, active learning experience and valuable cross-cutting and transferrable skills, including communication and interdisciplinary collaboration, that are in high demand in many industries and sectors.
Developed and delivered by leading academics at the UCL Institute for Sustainable Heritage, in collaboration with UCL Department of Statistical Science, industry and major national and international heritage institutions.
We are part of The Bartlett School of Environment, Energy and Resources – home to specialist institutes in energy, environment, resources and heritage. The Bartlett is the UK's largest multidisciplinary Faculty of the Built Environment, bringing together scientific and professional specialisms required to research, understand, design, construct and operate the buildings and urban environments of the future.
The Bartlett is #1 for Built Environment studies in the world (QS World University Rankings). The Bartlett’s research received the most world-leading ratings for research on the Built Environment in the UK in the most recent Research Excellence Framework.
Department: Bartlett School of Environment, Energy & Resources
Application and next steps
Applications
Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.
Who can apply?
You will have an engineering, computer science, applied mathematics or science background. Candidates with suitable professional experience or a social science or humanities background can also apply if they have a demonstrable computational or data analysis capability.
Application deadlines
- Full-time
- 24 July 2020
- Part-time
- 28 August 2020
- Flexible/Modular
- 28 August 2020
For more information see our Applications page.
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Page last modified on 18 December 2019