Semester 2

Core modules

• Service-Oriented Architectures
• Personal and Group Skills - this can be taken in either Semester

Option modules

Choose three option modules from:

• Analysis and Design of Algorithms
• Big Data and Predictive Analytics
• Foundations of Cybersecurity
• AI for Space
• Algorithms for Bioinformatics
• Service Design

Modules shown represent choices available to current students. The range of modules available and the content of any individual module may change in future years.

Optional Industry Placement

Studying a 'with industry' masters degree is the perfect way for you to enhance your career prospects and deepen your understanding of your specific area of expertise. By experiencing real-world scenarios and applying your skills and knowledge to business challenges, you will gain a unique insight into how your studies can be utilised in industry. This will not only showcase your abilities to future employers, but will also enhance your studies when you return to University to complete your programme.

• Find out more about industrial placements

Modules shown represent choices available to current students. The range of modules available and the content of any individual module may change in future years.

Individual Project

Your course will conclude with an (after your industry placement, if you have taken one). Using the experience gained during your course/placement, you will choose a challenging problem to work on and explore it by privately studying under the supervision of a member of the academic staff. (Students studying for a PGDip don't complete a project.)

A Selection of Recent MSc Projects

Network Analysis with MapReduce

Data about interactions between different entities can often be modelled as a network (consider, e.g., social networks or protein interaction networks). Centrality metrics are often used to identify the most important nodes of a network. As network data sets have been increasing in size, their analysis requires increasing amounts of computation time and memory. This project developed and evaluated scalable, distributed implementations of centrality calculations using the Hadoop MapReduce software framework.

Robust and Energy Efficient Wireless Sensor Networks

Wireless sensor networks are a new technology with a wide range of applications (from environment monitoring to surveillance) but subject to a number of energy constraints, many-to-one flows, and redundant low-rate data. Many routing protocols have been proposed for sensor networks that aim at achieving energy efficiency, but with little or no attention to robustness and fault-tolerance. This project addressed fault tolerance in ad hoc sensor network routing, namely the design and development of a robust and energy efficient routing protocol that distinguishes between local and large-scale traffic. A new multipath routing protocol was proposed and simulated which follows the 'number of hops' metric and employs a waiting time before transmitting messages to sink.

Modules shown represent choices available to current students. The range of modules available and the content of any individual module may change in future years.