A three-year degree programme comprising 22 core modules (144 credit points) across programming, computer systems, data and artificial intelligence, preparing graduates for careers in software development, AI, cyber security and data analytics.
Programme Overview
Computer scientists create the software, systems and technologies that organisations rely on every day. From mobile applications and artificial intelligence to cybersecurity and digital infrastructure, they use computing to solve problems, develop new solutions and improve the way people, businesses and communities work.
The Bachelor of Computer Science gives you the knowledge and skills to become part of this rapidly evolving field. You’ll learn how computers, software and digital systems work, then apply that knowledge to solve practical problems, design innovative solutions and develop technology for a wide range of applications. You’ll build foundations in programming, problem solving, networks, databases, web technologies and software development while learning how to work effectively in teams and communicate with project stakeholders.
You don’t need prior coding experience to get started. In your first year, you’ll study alongside students from Information Technology and Business Information Systems, giving you the opportunity to explore different technology disciplines before choosing a major. You can also choose a broader study pathway, developing advanced computer science knowledge and professional skills without specialising in a specific area.
Graduates are prepared for careers in software development, artificial intelligence, cybersecurity, data analytics and other technology-focused roles across a wide range of industries.
Programme Aims
The programme aims to:
- Build a firm foundation in mathematics, programming and the core principles of how computers, networks and databases work.
- Develop the ability to analyse problems and to design, build and test reliable, secure software using modern development methods.
- Equip students with applied skills in artificial intelligence, machine learning and big data, from classical AI through to generative models and large-scale analytics.
- Embed cyber security and data protection as a core concern throughout the degree, from first-year fundamentals to advanced practice.
- Foster the ethical, legal and social awareness needed to use AI and data responsibly.
- Develop teamwork, project management and communication skills for working with clients, users and other stakeholders.
- Prepare graduates for technology-focused careers across a wide range of industries, and for further postgraduate study.
Core Modules
| Code | Module | Credits |
|---|---|---|
| MATH180 | Mathematics, Statistics and Experimental Design | 6 |
Students entering scientific programmes need confidence with essential mathematical topics so they can apply them throughout their studies. Statistics and experimental design form the backbone of empirical research, providing the logical framework for collecting, analysing and interpreting data. A properly structured experiment minimises bias, isolates the variables being tested, and uses statistical methods to draw valid, reproducible conclusions. | ||
| PHYS100 | Physics | 6 |
The study of physics is challenging, but it is also richly rewarding. Students are challenged by the need to train their minds to look at the world in a different way, and to become masters at applying logic and advanced mathematics to problems. They are rewarded with the thrill of accomplishment in understanding the theories of great scientists like Newton, Maxwell, and Einstein. | ||
| CS100 | Fundamental Programming with Python | 6 |
An entry-level module establishing programming fluency using Python. Students learn to translate problems into working code through variables, control flow, functions, and basic data structures, building the foundation on which every later module depends. | ||
| CS102 | Computing and Cyber Security Fundamentals | 6 |
A foundation module covering how computers, operating systems, and networks work, paired with an introduction to the security principles that protect them. Positions security as a first-class concern from the outset rather than an afterthought bolted onto later modules. | ||
| CS106 | System Analysis | 6 |
Introduces the process of investigating business and user requirements and translating them into a specification for a software system. Covers requirements-gathering techniques and modelling notations used to describe systems before they are built. | ||
| CS110 | Database Management Systems | 6 |
Covers the theory and practice of relational database management systems: how data is modelled, stored, and queried reliably. Establishes the relational foundation that later modules (Database Systems, Big Data) build upon. | ||
| CS114 | Networks and Communications | 6 |
Explains how data moves between computers, from physical transmission media through to application-layer protocols. Builds the networking literacy needed for later modules touching cloud infrastructure and distributed big-data systems. | ||
| CS116 | Introduction to Web Technology | 6 |
A practical introduction to building web applications, covering both client-side presentation and the basics of server-side interaction. Gives students the tooling to build interfaces for projects in later modules. | ||
| Code | Module | Credits |
|---|---|---|
| CS200 | Algorithms and Data Structures | 6 |
Develops the analytical core of computer science: how to structure data efficiently and reason about the performance of the algorithms that operate on it. Essential preparation for machine learning and big-data modules later in the programme. | ||
| CS202 | Object Oriented Design and Programming | 6 |
Moves students from procedural to object-oriented thinking, covering both the design principles and the practical implementation of OOP in a mainstream language. Underpins the software engineering practice used in later applied modules. | ||
| CS210 | Database Systems | 6 |
Extends first-year database foundations into more advanced and alternative data-storage architectures, including distributed and non-relational systems. Bridges directly into the Big Data Systems module in Year 3. | ||
| CS220 | IT Project Management | 6 |
Introduces the frameworks and soft skills required to plan, run, and deliver IT and software projects on time and within budget. Prepares students for the project-based capstone work later in the degree. | ||
| CS224 | Software Development Methodologies | 6 |
Surveys the major approaches used to organise software development work, from traditional plan-driven methods to modern Agile practice. Gives students a shared vocabulary and process awareness for team-based project work. | ||
| CS226 | Human Computer Interaction | 6 |
Examines how people interact with computer systems and how to design interfaces that are usable, accessible, and effective. Introduces evaluation methods that feed into user-centred design practice in project modules. | ||
| CS228 | Java Programming | 6 |
A dedicated language module giving students depth in Java as a widely used, strongly typed, object-oriented language, complementing the earlier Python-based foundation with enterprise-oriented programming practice. | ||
| CS230 | Introduction to Artificial Intelligence | 6 |
The gateway AI module, introducing the classical foundations of the field — search, knowledge representation, and reasoning — before students move into applied machine learning and generative AI in Year 3. | ||
| Code | Module | Credits |
|---|---|---|
| CS310 | Advanced Programming | 8 |
Consolidates and extends programming practice with more advanced language features, design patterns, and performance considerations, preparing students to write production-quality code for capstone-level projects. | ||
| CS320 | Cyber Security | 8 |
An advanced, standalone security module building on the Year 1 fundamentals module, covering offensive and defensive techniques, security architecture, and governance in greater depth. | ||
| CS326 | Generative AI | 8 |
Focuses on the architectures and applications behind modern generative systems — large language models, diffusion models, and related techniques — along with practical prompt engineering and system integration. | ||
| CS330 | Machine Learning and Data Mining | 8 |
Covers the core algorithms and workflow of machine learning and data mining, from supervised and unsupervised learning through to model evaluation, giving students the applied AI skillset that sits alongside the generative AI module. | ||
| CS340 | Big Data Systems and Analytics | 8 |
Addresses the infrastructure and analytical techniques needed to store, process, and extract insight from data at scale, distinct from the algorithmic focus of the Machine Learning module and building on the distributed-systems groundwork laid in Year 2. | ||
| CS360 | AI and Data Ethics | 8 |
A capstone-adjacent module examining the social, legal, and ethical implications of AI and large-scale data use, ensuring graduates can deploy the technical skills from the AI and Big Data modules responsibly. | ||
Total: 22 modules (144 credit points) across three years.