Description

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.

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.

Study Details:

MSc Full-time: 1 year Part-time: 2 years Flexible: up to 5 years Options: PG Dip

Module Details:

Compulsory modules

Students will take three compulsory modules in the first term and a fourth one in the second term.

  • Introduction to Sustainable Heritage (15 credits
  • Introduction to Statistical Data Science (15 credits)
  • Introduction to Heritage Science (15 credits)
  • Heritage Data Mapping and Visualization (15 credits)
  • Heritage Data Management (15 credits)

Optional modules

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)

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.

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.

For further and the most up-to-date information about the programme structure please visit the UCL Prospectus website.

Programme Funding

UCL offers a range of financial awards aimed at assisting both prospective and current students with their studies.

Student Destinations

Data science is in high demand in many different industries. As a graduate of this programme you will be ideally placed to gain employment as a data scientist, particularly in sectors that foster transdisciplinarity and break barriers between technology, humanities and social sciences.

The programme has been developed with input from industry leaders from several sectors including architecture, heritage and digital technologies. From these industries you will gain exposure to real-world challenges of generating, using, and interpreting data. You will also develop a skill set in data science that will be highly transferable across these and many other sectors.

Employability

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.

Fees

For fees and funding options, please visit website to find out more

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.

International equivalent qualifications are considered, they can be found by selecting the relevant country from a drop down list in the entry requirements section of each programme page in the UCL Graduate Prospectus.

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

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Key Information

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University Hub

University College London – The Bartlett

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