A UK first or upper second class honours degree or equivalent in ANY degree that is not highly quantitative, including those in social sciences, the arts and humanities
Evidence of competence in written and spoken English if the applicant’s first language is not English:
- minimum TOEFL requirement is 102 IBT (no element under 23)
- minimum IELTS score is 7.0 overall with no element under 6.0 or equivalent
Months of entry
The Master of Data Science (Digital Humanities) is a conversion course with a hard-core of data science, intended to provide Masters-level education rich in the substance of data science for students who hold a first degree in the Humanities. All around us, massive amounts of increasingly complex data are being generated and collected, for instance, from mobile devices, cameras, cars, houses, offices, cities, and satellites. Business, research, government, communities, and families can use that data to make informed and rational decisions that lead to better outcomes. It is impossible for any one individual or group of individuals to keep on top of all the relevant data: there is simply far too much. Data science enables us to analyse large amounts of data effectively and efficiently and as a result has become one of the fastest growing career areas.
Previously, data science was the province of experts in maths and computer science, but the advent of new techniques and increases in computing power mean that it is now viable for non-experts to learn how to access, clean, analyse, and visualize complex data. There is thus a growing opportunity for those already in possession of knowledge about a particular subject or discipline, and who are therefore able to grasp the full meaning and significance of data in their area, to be able to undertake data analysis intelligently themselves. The combination of primary domain knowledge with an expertise in extracting relevant information from data will give those with this ‘double-threat’ a significant employment advantage.
Introductory modules are designed to bring students who are complete beginners and will require no prior knowledge of mathematics or programming up to speed with the background necessary for data science. This is done on a need-to-know basis, focusing on understanding in practice rather than abstract theory. Data Science core modules will include an introduction to mathematics for Data Science, statistical modelling (in R), computer programming (in Python), machine learning, AI and neural networks.
In addition to that Data Science core, you will also take a module in Digital Humanities which will explore the application of quantitative and computational methods to cultural data: languages, literary, philosophical and theological texts, historical data, artifacts and material culture, visual art, video and music. Alternatively, you may take a traditional MA module in your area of interest (subject to departmental approval and timetabling).
Optional modules allow students to focus on an area of interest.
The degree provides training in relevant areas of contemporary data science in a supportive research-led interdisciplinary learning environment. The broad aims are:
- To develop advanced and systematic understanding of the complexity of data, including the sources of data relevant to science, alongside appropriate analysis techniques
- To enable students to critically review and apply relevant data science knowledge to practical situations
- To develop a critical awareness of current issues in data science which is informed by leading edge research and practice in the field
- To develop a conceptual understanding of existing research and scholarship to enable the identification of new or revised approaches to data science practice
- To develop creativity in the application of knowledge, together with a practical understanding of how established, advanced techniques of research and enquiry are used to develop and interpret knowledge in data science
- To develop the ability to conduct research into data science issues that requires familiarity with a range of data, research sources and appropriate methodologies and ethical issues.
- To develop advanced conceptual abilities and analytical skills in order to evaluate the rigour and validity of published research and assess its relevance to new situations
- To extend the ability to communicate effectively both orally and in writing, using a range of media.
The course is designed around a pedagogical framework which reflects the core categories of the data science discipline.
A number of subjects can be identified and defined within each application domain. Whilst a Masters degree cannot incorporate all subjects, a selection of subjects representative of each domain ensures that the course incorporates the necessary breadth and depth of material to ensure a skilled graduate.
The Masters allows for progressive deepening in your knowledge and understanding, culminating in the research project which is an in-depth investigation of a specific topic or issue where you will apply the techniques you have learned from your Data Science modules to a research problem in a Humanities domain of your choosing.
The global dimension is reinforced through the use of international examples and case studies where appropriate.Course Structure
The Master of Data Science (Digital Humanities) degree is comprised of the following core modules:
- Introduction to Computer Science
- Introduction to Statistics for Data Science
- Machine Learning
- Programming for Data Science
- Introduction to Mathematics for Data Science
- Digital Humanities: Theory and Practice
- Research Project (60 credits).
Examples of optional modules:
- Ethics and Bias in Data Analytics
- Text Mining and Language Analytics
- Data Exploration, Visualization, and Unsupervised Learning
- Strategic Leadership.
Fees and funding
Scholarships available for 2022 entry will be determined in September 2021. Over 60 scholarships are available, each year. Some scholarships are awarded to more than one person. For further information see the course listing.
Qualification, course duration and attendance options
- full time12 months
- Campus-based learningis available for this qualification
Course contact details