UCL’s Data Science for Cultural Heritage MSc provides an innovative opportunity to study data science through the exciting lens of cultural heritage. It is the first MSc in the world to provide in-depth, practice-based data science training in a cultural heritage context. The MSc will equip you to succeed as a data scientist in fields such as heritage science, the built environment, digital technologies and media, analytics, and software.
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.
Tuition fees (2021/22)
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: ucl.ac.uk/students/fees. Fees for flexible, modular study are charged pro-rata to the appropriate full-time Master's fee taken in an academic session.
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
UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level. International Preparation Courses
Further information can be found on our English language requirements page.
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 five compulsory modules (75 credits), three optional modules (45 credits) and an individual research dissertation (60 credits).
A Postgraduate Diploma of five 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.
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.
Students will take four compulsory modules in the first term and a fifth one in the second term.
- Introduction to Sustainable Heritage (15 credits
- Introduction to Statistical Data Science or Statistics for Heritage Science (15 credits)
- Introduction to Heritage Science (15 credits)
- Heritage Data Mapping and Visualization (15 credits)
- Heritage Data Management (15 credits)
Students will take three optional modules with the total value of 45 credits.
- Heritage Imaging (15 credits)
- Crowd Sourcing and Citizen Data for Cultural Heritage (15 credits)
- Machine Learning for heritage (15 credits)
- Environment Material Interactions (15 credits)
- Technologies and digital approaches for built heritage (15 credits)
- Further information about these modules is available on the department website.
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.
You will require your own laptop. Recommended specifications can be provided on request.
For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs.
UCL offers a range of financial awards aimed at assisting both prospective and current students with their studies.
Any additional funding is offer by our faculty, The Bartlett, are advertised on their scholarships page.
Scholarships relevant to this department are displayed below.
- Deadline: 31 May 2021
- Tuition fees plus £15,364 maintenance/yr (Duration of programme)
- Based on financial need
- Deadline: 5pm (BST), Thursday 21 April 2021
- £10,000 (1 year)
- Based on both academic merit and financial need
For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.
Data science is in high demand in many and diverse industries. As a graduate of MSc DaSCH you will be ideally placed to gain employment as a data scientist, in particular in those sectors that foster transdisciplinarity 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 and digital technologies. You will gain exposure to real data challenges from these industries and will develop skills in data science that are highly transferable across these and many other sectors. Additionally, you will also develop skills as a researcher which will prepare you for a career in academia should you be interested.
Transdisciplinarity, 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 and complex 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 and active learning experience. You will gain valuable transferrable skills, such as communication and interdisciplinary collaboration. These skills are in high demand in many industries and sectors.
To ensure the programme is industrially relevant, we have an advisory board. Members of our advisory board come from major national and international institutions including the Turing Institute, British Library, ARUP, and Historic England. They also contribute your teaching and supervision, as well as providing datasets and research ideas.
UCL Institute for Sustainable Heritage is part of The Bartlett School of Environment, Energy and Resources – home to specialist institutes in energy, environment, resources and heritage. The Bartlett Faculty brings together scientific and professional specialisms required to research, understand, design, construct and operate the buildings and urban environments of the future.
The QS World University Rankings (2021) ranks The Bartlett, our faculty, as #1 for Architecture/Built Environment studies in the UK and #2 in the World. 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.
Application and next steps
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.
This programme requires two references. Further information regarding references can be found in our How to apply section.
There is an application processing fee for this programme of £90 for online applications and £115 for paper applications. Further information can be found at: ucl.ac.uk/prospective-students/graduate/taught/application.
Who can apply?
You will have an engineering, computer science, applied mathematics or science background. We encourage candidates with suitable professional experience or a social science or humanities background to apply if they have a demonstrable computational or data analysis capability.
- 31 May 2021
- 31 May 2021
- 31 May 2021
For more information see our Applications page.Apply now
What are we looking for?
When we assess your application we would like to learn:
- why you want to study Data Science for Cultural Heritage at graduate level
- why you want to study Data Science for Cultural Heritage at UCL
- what particularly attracts you to the chosen programme
- how your academic and professional background meets the demands of this challenging programme
- where you would like to go professionally with your degree
Together with essential academic requirements, the personal statement is your opportunity to illustrate whether your reasons for applying to this programme match what the programme will deliver.
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