Advanced Computer Science (Cloud Computing) MSc

The following modules are available in 2023/24 for the Advanced Computer Science (Cloud Computing) MSc and are examples of the modules you are likely to study. All Modules are subject to change. You will study 180 credits in total.

Compulsory modules:

MSc project – 60 credits
You will undertake a research project during the summer months.

Recent projects include:

  • Intelligent services to support sensemaking
  • Machine Learning based cloud resource scheduling
  • Energy-aware resource management
  • Scalable serverless workflows

Advanced Software Engineering – 15 credits 

In this module, you will build on prior knowledge of software engineering principles, expanding it to include a more thorough understanding of what constitutes good design. You will learn how design can be improved through the use of patterns and refactoring, and you will gain a broad appreciation of the different architectural styles used in modern software.

Cloud Computing Systems – 15 credits

On completion of this module, you should be able to: demonstrate an understanding of cloud computing techniques and technologies; demonstrate an understanding of the contexts in which big data systems are applied; identify the paradigms that determine the requirements, capabilities and performance of Cloud systems; and design a high-level framework of a Cloud architecture.

Optional modules include:

​​​​​​Knowledge Representation and Reasoning – 15 credits

Analysing descriptions of complex real world scenarios in terms of formal representation languages. Understanding automated reasoning and ontology as well as their applications. 

Programming for Data Science – 15 credits

This module is designed to give those with little or no programming experience a firm foundation in programming for data analysis and AI systems, recognising a diversity of backgrounds. The module will also fully stretch those with substantial prior programming experience (e.g. computer scientists) to extend their programming and system-building knowledge through self-learning supported by on-line courseware.

Blockchain Technologies – 15 credits

This module provides a comprehensive knowledge on fundamentals and practical aspects of distributed ledgers and their applications in society. Starting from required knowledge on distributed systems and security, this module moves to the “big picture” of the different blockchain architectures that have been evolving in this dynamic technological landscape.

Machine Learning – 15 credits

On completion of this module, you should be able to: list the principal algorithms used in machine learning, and derive their update rules appreciate the capabilities and limitations of current approaches; evaluate the performance of machine learning algorithms; and use existing implementation(s) of machine learning algorithms to explore data sets and build models.

The full list of module information can be read in the course catalogue.