Computer Science MSc

The primary aim of this module is to develop student’s employability skills and support their search for a placement. The module will include communication, team working, negotiation and problem-solving skills development as well as practical workshops on selection methods, CV's, cover letters, interview preparation and techniques. The module will also introduce students to other methods that will include aptitude test and assessment centres.

The Cybersecurity & Cloud Systems module is designed to provide students with a comprehensive understanding of both cybersecurity and cloud-based systems, exploring their distinct features and how they collaboratively contribute to the security and management of modern digital environments. This module aims to provide hands-on experience in developing the key skills required to understand and circumvent cyber incidents through the implementation of secure systems and data, along with exposure to cloud platforms and security practices.

The module aims to provide an overview of the fundamental principles used in the emerging area of Intelligent Environments. These fundamental concepts will be illustrated with examples and small projects.

This module aims to equip students with the skills to design, develop, test, and implement decentralised applications (dApps) using various blockchain tools, libraries, and techniques. It covers smart contract development, blockchain integration, security best practices, and performance optimisation, enabling students to build secure and efficient dApps across different platforms.

Machine Learning (ML) is ubiquitous in Information Technology. It works very well and does seemingly magic things. However, software engineers very often feel uncomfortable about this phenomenon because it does not adhere to the philosophy and engineering principles of Computer Science. Consequently, there are numerous research and development activities around integrating ML into Software Engineering. In this course, we introduce the main principles of ML and use them practically on applications. We then swiftly move on to explore the synergies between ML and Software Engineering. This course fosters competencies on ML concepts and ML implementation skills as well as critical understanding of the opportunities and difficulties of integrating ML and Software Engineering.

The module presents modern software development practices with a focus on agile methodologies and iterative development. The module explores the entire software development lifecycle (SDLC), incorporating domain analysis, requirements capture, architectural considerations, design, coding practices, versioning, testing and deployment. All phases of the SDLC are presented within a context of agile approaches to project management that stress the importance of incremental delivery and continuous feedback whilst striving for end-user value.

This module aims to equip students with the tools and techniques necessary to design and implement effective and efficient experiments in the context of computer science projects. It will also provide students with the skills required for statistical analysis, hypothesis testing, and presenting results within the scope of computational research and data-driven studies.

We aim to model a wide range of teaching strategies and approaches on the course which you can adapt to your own setting.

Teaching

You'll be taught by an experienced teaching team with a wide range of expertise and professional experience. Our learning activities include lectures, presentations, webinars, one-to-one tutorials and group projects.

The course is delivered by our academics and industry partners in person and via digital platforms. You’ll attend face-to-face classes (with some online sessions) comprising a mixture of labs, seminars and lectures. In these sessions you'll do practical work designing, analysing and building software, which will give you the opportunity to ask questions, take part in class discussions and talk about problems.

Typical weekly breakdown

A typical week looks like this:

Learning terms

On-campus: This includes tutor-led sessions such as seminars, lab sessions and demonstrations as well as supervised or student-led sessions for work in small groups.

Online learning: This is teaching that is delivered online using tools like Skype or Zoom, as well as work that you do yourself using online teaching resources.

Independent study: This is the work you do in your own time including reading and research.

Part-time study

You can also study this course part-time over two years.

You will be based at our north London campus in Hendon.

There are no written exams but there may be some tests. Your coursework including presentations, reports, learning logbooks, practical software development assignments and group assignments will be assessed.

For your independent project and dissertation, you will plan, conduct and critically evaluate an analytical study. You will be assessed on your research proposal, written dissertation and a viva voce exam, in which you’ll justify and reflect on your work, and discuss possible further developments.

Academic support

We have a strong support network online and on campus to help you develop your academic skills. We offer one-to-one and group sessions to develop your learning skills together with academic support from our library, IT teams and learning experts.

Feedback

To help you achieve the best results, we will provide regular feedback.

You'll evaluate your work, skills and knowledge and identify areas for improvement. Sometimes you'll work in groups and assess each other's progress.