Our MRes in Web Science and Big Data Analytics is highly flexible and specific, and is tailored to suit students' individual needs. It is problem-based learning and students will be encouraged to bring up their own technical problems for research, though not required. They will be able to choose their optional modules from a wide range of specialised options, including programming and analytical modules, and will research and write a dissertation based on a research project. It is a cost-effective way of addressing a specific technical problem the industry is facing. More specifically,
students will start with a specific problem and choose modules based on the needed knowledge,
then be liaised with their academic or industrial supervisor to choose a study area of mutual interest, and
research and write a dissertation based on a 10-month research project.
It is intended for students who have a background in the Internet-based businesses (though not essential) and who have a specific technical question in mind for a substantial research project. We also offer the more Teaching orientated MSc Web Science and Big Data Analytics.
In our MRes Web Science and Big Data Analytics, students will gain a detailed knowledge and understanding of the fundamental principles and technological components of the World Wide Web and essential computational and statistical skills; they will not only learn the state of the art (Web) search and information retrieval technologies and their underlying computational and statistical methods, but also study essential large-scale data analytics to discover and extract insights, patterns, and useful knowledge from vast amounts of unstructured data produced daily by (Web) users and systems in various fields.
Career Prospects
MRes Web Science and Big Data Analytics' unique combination of technical skills makes graduates well equipped to proceed to scientific research or the ideal choice for the best employers in Internet related industries and the areas requiring large-scale data analytical skills.