MSc Computer Science

What you will study

The MSc Computer Science programme aims to build upon the knowledge acquired at undergraduate level, providing you with advanced skills and expertise in specific areas of computer science.

You will delve deeper into specific areas of computer science, such as artificial intelligence, machine learning, and data science, and engage in research to contribute to the advancement of knowledge in computer science.

Using advanced theories, you will be able to solve complex problems, lead software development projects and address real-world software engineering challenges.

Students will study 6 core modules of 20 credits each, followed by a 60 credit dissertation, making a total of 180 credits.

MODULES

• Artificial Intelligence: You will gain an in depth understanding of advanced concepts, algorithms, and techniques used in artificial intelligence (AI). You will also develop practical skills in designing and evaluating complex AI systems to solve real-world problems, using advanced programming languages, tools, and frameworks. Through independent research, critical analysis, and effective communication, this will enable you to contribute to the knowledge and practice of AI and its applications in various domains.
• Secure Computing: This module provides a holistic understanding of key topics in computer security which includes risks and threats, crytography and information security governance. You will explore how to identify vulnerabilities, encryption algorithms, secure coding practices, user authentication methods and effectively managing risks and incidents. This will equip you with the knowledge and skills necessary to navigate computer security and to implement robust security measures.
• Advanced Data Structures and Algorithms: This will explore the theories and application of key computer programming concepts such as algorithms, abstract data types, underlying data structures and their integration to produce efficient code. You will develop the knowledge and skills to be able to analyse problems and then design, implement, and analyse, effective algorithmic solutions using a suitable programming language. You will also learn the implications of algorithmic solutions in terms of their computational complexity (space, time and logical) and develop a working knowledge of optimal and approximate (including heuristic) solutions to problems.
• Advanced Machine Learning: You will be introduced to the practical challenges of applying machine learning techniques to real-world problems. Building on existing programming knowledge, you will gain an understanding of the key concepts, methodology and techniques required to develop effective algorithms to analyse large data sets. This will be combined with the analysis techniques required to compare, select and justify the use of appropriate machine learning methods whilst developing programmed solutions.
• Database Systems and Data Analytics: This module will deepen an understanding of the role of database systems in Information Management, and the theoretical and practical issues that influence the design and implementation of database management systems. You will develop the skills required to create, maintain, and interrogate a relational database management system using commercially available database software. This will expand on your knowledge of database systems and data analytics by introducing them to several advanced topics and techniques including data science and data analytics.
• Research Methods for Digital Technologies: You will gain the underpinning skills to ensure that competent work and standards are achieved throughout your chosen programme of study. This will include the development of professional level information handling and analysis skills, so that you become proficient at planning and managing your own research projects.
• Dissertation: You will have gained a comprehensive understanding of key concepts, methodologies, and approaches within your chosen field of study. and will conduct studies to further contribute to knowledge. This module will support students in carrying out an independent project under the supervision of the programme team staff on a topic which is related to their degree programme specialism.

The information listed in this section is an overview of the academic content of the programme that will take the form of either core or option modules. Modules are designated as core or option in accordance with professional body requirements and internal academic framework review, so may be subject to change.

Entry requirements & applying

The standard entry requirements for these programmes are an honours degree of at least 2:2 classification in a Computer Science related subject area, or equivalent in any science-based degree with a strong computing and/or engineering element. In some cases, applicants with substantial commercial or industrial experience can be accepted, subject to interview and references.

Teaching & Assessment

Teaching and Assessment

Teaching

The computing program suite employs a diverse range of cutting-edge industry tools and software, complemented by innovative teaching methods. This dynamic approach not only imparts industry-relevant skills but also empowers students to elevate their work to new heights when possible. The entire staff enthusiastically embraces the active learning framework (ALF), resulting in numerous enhancements to the teaching and learning experience.

Assessment

Assessments in computer science at the university level are designed to evaluate students' understanding, application, and proficiency in various aspects of the discipline. These assessments encompass a diverse range of methods, including:

• Coursework and Projects: Assignments and projects provide hands-on experience, allowing students to apply theoretical knowledge to real-world scenarios. This may include software development projects, research papers, or problem-solving tasks.
• Coding Assignments: Practical coding assignments assess students' programming skills, logical reasoning, and ability to develop efficient and effective code.
• Group Projects: Collaborative projects evaluate teamwork, communication, and the ability to work in diverse teams, reflecting the collaborative nature of the tech industry.
• Presentations: Students may be required to present their findings, solutions, or project outcomes, enhancing their communication and presentation skills.
• Laboratory Work: Practical sessions in computer labs assess students' ability to apply concepts, troubleshoot issues, and work with various tools and technologies.
• Problem-solving Exercises: These exercises challenge students to solve complex problems, encouraging critical thinking and analytical skills.
• Reports and Documentation: Writing reports or documenting project processes assesses students' ability to communicate technical information clearly and concisely.

Personalised Support

The department follows a well-established open-door approach, actively interacting with students, alumni, and industry stakeholders. Essential information and communication avenues are facilitated through tools like Teams and Moodle. Additionally, every student is assigned a personal tutor, fostering regular meetings, while additional personalized support is extended to part-time students through the Virtual Learning Environment (VLE).

Career prospects

Our Careers & Employability service is there to help you make decisions and plan the next steps towards a bright future. From finding work or further study to working out your interests, skills and aspirations, they can provide you with the expert information, advice and guidance you need.

One obvious advantage of a computer science master’s degree is that students become more employable. Jobs include, but are not limited to:

• Software Engineer/Developer
• Data Scientist/Data Analyst
• Artificial Intelligence/Machine Learning Engineering
• Research Specialist
• IT Consultant/Systems Analyst
• Project Manager/Technical Lead
• Software Architect.
  

Employability features built into the course:

• Advanced Technical Skills: Graduates acquire advanced technical skills in areas such as programming, data analysis, artificial intelligence, machine learning, and cybersecurity, making them highly desirable candidates for technical roles in various industries.
• Specialised Knowledge: The course offers opportunities for specialisation in specific areas of computer science, allowing graduates to tailor their expertise to match industry demands. This specialisation enhances their competitiveness in the job market.
• Research Experience: For students interested in research-oriented careers, the course offers opportunities to engage in research projects, contributing to advancements in the field and preparing students for roles in academia or industry research labs.
• Professional Development: the course includes modules or workshops focused on professional skills development, such as communication, teamwork, project management, and leadership, which are essential for success in the workplace.
• Networking Opportunities: Students have the chance to network with industry professionals, alumni, and fellow students through seminars, workshops, career fairs, and alumni events, expanding their professional network and potential job opportunities.

Fees & funding

You do not have to pay your tuition fees upfront.

The fees you pay and the support available will depend on a number of different factors. Full information can be found on our fees & finance pages. You will also find information about what your fees include in the fee FAQs.

All fees are subject to any changes in government policy, view our postgraduate fees.

International

This course is open to international students, for information about the university’s entry requirements for EU/international students, please visit our international section.