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Pre-Masters in Science and Engineering (3-Terms)
The Pre-Masters in Science and Engineering prepares you for a choice of progression degrees at Durham University. You will join the programme for two or three terms, depending on your academic qualifications and level of English language.

You can choose from a range of postgraduate degrees to progress to when you complete this course.

Who is this programme for?

This programme is for international students looking to study a postgraduate science or postgraduate engineering degree at Durham University.

• Choose from a range of degrees within the Department of Engineering, Department of Mathematical Sciences and Department of Psychology.
• Develop your academic knowledge, English language level and study skills needed for progression to your chosen Masters degree.
• Study core modules and designated modules related to your chosen specialism.
• Adjust to the UK education system and explore concepts, terminology and advanced academic writing needed to succeed at postgraduate level.
• Develop practical skills with research projects and laboratory experience.

What will you study?
You will study core modules, which cover general skills, and designated modules that relate to your progression degree. This means your Pre-Masters programme will be tailored to the specific needs of your chosen degree.

Core modules

• Academic English Skills
• Advanced Academic Writing and Critical Reading
• Quantitative Skills
• Extended Research Project

When you complete the Pre-Masters in Science and Engineering, and achieve the required grades, you can progress Master of Data Science (Digital Humanities) at Durham University.

Leading to: Master of Data Science (Digital Humanities)

The Master of Data Science (Digital Humanities) is a 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).