During the first half of the year you will study core modules which will lay the foundations of the programme by giving you an understanding of the key topics of software engineering and systems programming.
From there you’ll have the chance to tailor your studies to suit your own preferences. You’ll choose from a wide range of optional modules on diverse topics such as machine learning, big data systems, and scientific computation.
In the second half of the year, over the summer months, you’ll also work on your research project. This gives you the chance to work as an integral part of one of our active research groups, focusing on a specialist topic in computer science and selecting the appropriate research methods.
Research projects include various topics such as edge computing, serverless architectures, big data, energy efficiency and resource management.
The professional project is one of the most satisfying elements of this course. It allows you to apply what you’ve learned to a piece of research focusing on a real-world problem, and it can be used to explore and develop your specific interests.
Recent projects for MSc Advanced Computer Science (Cloud Computing) students have included:
Intelligent services to support sensemaking
Machine Learning based cloud resource scheduling
Energy-aware resource management
Google cloud data analysis
Cloud Computing auto-scaling
Scalable serverless workflows
A proportion of projects are formally linked to industry, and can include spending time at the collaborator’s site over the summer.
Want to find out more about your modules?
Take a look at the Advanced Computer Science (Cloud Computing) module descriptions for more detail on what you will study.
The list shown below represents typical modules/components studied and may change from time to time. Read more in our Terms and conditions.
For more information and a full list of typical modules available on this course, please read Advanced Computer Science (Cloud Computing) MSc in the course catalogue
Advanced Software Engineering
Optional modules (selection of typical options shown below)
Big Data Systems
Knowledge Representation and Reasoning
Programming for Data Science
Data Mining and Text Analytics
Learning and teaching
Our groundbreaking research feeds directly into teaching, and you’ll have regular contact with staff who are at the forefront of their disciplines. You’ll have regular contact with them through lectures, seminars, tutorials, small group work and project meetings.
Independent study is also important to the programme, as you develop your problem-solving and research skills as well as your subject knowledge.
Our Virtual Learning Environment will help to support your studies: it’s a central place where you can find all the information and resources for the School, your programme and modules.
You can also benefit from support to develop your academic skills, within the curriculum and through online resources, workshops, one-to-one appointments and drop-in sessions.
On this course you’ll be taught by our expert academics, from lecturers through to professors. You may also be taught by industry professionals with years of experience, as well as trained postgraduate researchers, connecting you to some of the brightest minds on campus.
You’ll be assessed using a range of techniques which may include case studies, technical reports, presentations, in-class tests, assignments and exams. Optional modules may also use alternative assessment methods.