Taught course

Computer Science

Queen Mary, University of London · Computer Science

Entry requirements

A 2:1 or above at undergraduate level in Electronic Engineering, Computer Science, Mathematics or a related discipline.

Good 2:2 degrees are considered on an individual basis.

Months of entry


Course content

The demand for better products and commercial services drives the search for creative solutions using computing-based systems, and has established a critical dependence between computing and practically every industry and sector. This flexible programme offers a broad range of advanced study options, reflecting the emerging technologies in industry.

This is a multidisciplinary programme and, in addition to pure computer science modules, you may choose options where computer science intersects with other fields and build on your first degree.

This course is ideal for students with a good first degree in electronic engineering, computer science, maths or a related discipline. You will gain experience in functional programming, and learn about semi-structured data and advanced data modelling. You will gain a basis in security and authentication, as well as Bayesian decision and risk analysis. Beyond this, you will be able to personalise your programme through a wide range of employment-relevant module choices.

Modules include:

  • Machine Learning
  • Natural Language Processing
  • Interactive Systems Design
  • Data Analytics using Python and its associated libraries for data wrangling and Machine Learning

Our excellent links will allow you to build your network with industry and potential employers and have opportunities to work together on commercial and research projects.

Qualification, course duration and attendance options

  • MSc
    full time
    12 months
    • Campus-based learningis available for this qualification
    part time
    24 months
    • Campus-based learningis available for this qualification

Course contact details

School of Electronic Engineering and Computer Science
+44 (0)20 7882 7333