Computer Science

30 credits

This module builds upon foundational computing knowledge to develop technical competencies in networks and operating systems. You will gain understanding of the infrastructure that underpins modern digital environments, cloud services, and enterprise IT operations. The module emphasises both theoretical principles and practical implementation skills essential for professional IT practice.

Building on introductory computing concepts from Level 4, this module is delivered through integrated theoretical instruction and extensive hands-on laboratory work.

You will develop professional-level skills in network design, configuration, and management, alongside comprehensive understanding of operating system architecture, administration, and security hardening.

The module prepares you to address complex real-world challenges in enterprise IT environments and positions you for advanced study in specialised areas such as cloud computing, cybersecurity, and systems engineering.

Teaching and learning

This module adopts a project-centred, active learning approach designed to foster technical problem-solving, critical evaluation of infrastructure solutions, and professional application of networking and systems administration principles. The teaching strategy integrates theoretical seminars with practical workshops, supported by structured independent study and comprehensive formative feedback opportunities.

Seminars introduce networking and operating systems concepts, principles, and industry best practices, whilst workshops provide hands-on experience with network configuration, system administration tasks, security implementation, and troubleshooting exercises.  

Assessment

This module will be assessed by laboratory-based coursework (60%) and a team-based network and systems integration project (40%).

30 credits

This module introduces you to Artificial Intelligence (AI), exploring how computers make rational decisions, and Data Analytics, which involves interpreting diverse datasets to draw meaningful conclusions.

The module integrates fundamental AI concepts with practical data analytics techniques, preparing you for advanced study in machine learning and real-world applications in data science.

You will explore the fundamental question of how rational behaviour is defined and how challenging AI problems are defined. This includes understanding agent-based systems (computer programs that respond and change behaviour according to their environment) and stochastic problems (situations where randomness or uncertainty plays a role).

The module examines AI problem spaces (the entire set of possible states or moves a system can make) and methods for efficiently searching these spaces.

You will investigate complex scenarios like two-player games, constraint satisfaction problems (finding a solution while respecting specific limits or conditions), and probabilistic reasoning, including Bayes' theorem, that allow systems to make decisions under uncertainty. Ethical implications of AI decision-making, including fairness, transparency, and accountability, will also be considered throughout the module.

Alongside AI concepts, you will develop practical skills in data analytics. The module will ensure you have good skills in data wrangling (cleaning and transforming raw data), data visualisation, descriptive statistics, and exploratory data analysis (EDA).

You will also gain experience in identifying, sourcing, and collecting appropriate datasets, with consideration for data quality, ethics, and legal responsibilities such as data privacy and bias. You will work with various data sources, applying appropriate analytical tools and statistical tests to solve problems. The module further introduces how AI techniques, such as pattern recognition and basic machine learning approaches, can be applied within data analytics to extract insights from complex datasets.

Teaching and learning

Teaching combines seminars and practical laboratory sessions with problem-based learning activities, encouraging you to apply AI and data analytics methods to authentic challenges.

Seminars will introduce key concepts, algorithms, and theoretical foundations through discussion, worked examples, and group activities. Laboratories will focus on hands-on implementation of AI techniques and data analytics methods using appropriate programming languages and tools, including Python, relevant AI libraries, and data analytics frameworks.

Independent study, guided tasks, and continuous formative feedback support your confidence development. All resources and recordings will be accessible online.

Assessment

This module will be assessed by an AI problem solving and search video recording (50%) and a data analytics project (50%).

30 credits

This professional practice-focused module gives you the opportunity to use and enhance your programming skills while experiencing how complex software projects are developed and managed in industry.

In this module, you will work in a group throughout the term to create a full-stack web application using industry-standard Agile development methodology and modern project management techniques.

You will use industry-standard tools to create designs, establish shared development environments, design a database, develop, share, test code, and finally deploy and run your applications. In this experience of the entire software development lifecycle, you will need to combine and develop your full range of technical, professional, and teamwork skills.

Each week's class is divided into individual and group work sessions.

In your individual work, you will be introduced to a new skill or technique to develop. These will include introductions to commonly used frontend and backend development frameworks, as well as ‘devOps’ and testing technologies, for example Docker and GitHub Actions, Jest unit testing and Nightwatch browser automation.

In the group session, you will review your weekly tasks and goals and hold a supportive 'standup' meeting with your lecturer to ensure the project progresses efficiently and with equal participation. You may be allocated a specialist role in your group (for example, Project Manager, Frontend Developer, DevOps Engineer, Quality Assurance specialist), but you will also build skills and experience across all these areas.

You will be expected to communicate with your group between the weekly sessions to ensure that planned work is undertaken within the required timeframe.

Groups are encouraged to create a project that serves social and environmental purposes. This requires you to explore user needs, ethical and professional issues, and security. You will establish Personal Learning Journey (PLJ) goals at the beginning of the module and maintain a PLJ journal throughout. This will prepare you for your third year and enable you to start focusing on specialist areas for your Final Year project. A professional Agile coach will conduct a masterclass of best practices in project planning and teamwork.

This module will give you ample opportunity to become familiar with industry practice and build your professional portfolio.  You will be encouraged to reflect on your experiences and understand how these can support your job applications and interviews.

Teaching and learning

You will gather technical skills in lab sessions, that build on your earlier modules and immediately apply these to the group project via carefully planned weekly tasks.

The technical skills are supported by screencasts and lab sheets so that there are easy to use resources available for you to implement the technical features required for their projects.

Assessment

This module will be assessed by a group software development project (70%) and an individual reflective practice and mock interview (30%).

30 credits

This module significantly advances your software development skills, focusing on the design and implementation of complex software systems.

You will take a model-driven approach throughout, integrating Object-Oriented Programming (OOP), Unified Modelling Language (UML), and software design patterns to effectively bridge conceptual modelling and practical development.

You'll deepen your understanding of OOP principles, including abstraction, encapsulation, inheritance, and polymorphism, through hands-on Java programming. You will learn to capture requirements and design robust systems using UML diagrams such as Use Case, Class, Sequence, State Machine, Activity, Component, and Deployment diagrams. The module also introduces model-based testing, enabling you to validate UML models against their Java implementations.

You will gain proficiency in applying design patterns (creational, structural, and behavioural), understand their architectural implications, and employ them within the Model-View-Controller (MVC) framework. You'll use modern component-based frameworks to understand code reuse and integrate event-driven programming techniques to develop graphical user interfaces (GUI) using best design practices.

Throughout the module, emphasis is placed on best coding practices, testing and quality assurance using JUnit, and integrating emerging technologies such as AI into modern software development workflows.

You will gain comprehensive knowledge of robust system design, architecture, and industry-standard practices. This, combined with the analytical precision and advanced problem-solving methods taught, prepares you for advanced technology roles or further academic study.

Teaching and learning

Lectures will introduce modelling concepts, UML notations, design patterns, and architectural principles, whilst workshops will consist of hands-on modelling exercises, code implementation tasks, design pattern applications, and case study analyses.

This module adopts a model-driven, active learning approach designed to foster problem-solving, critical thinking, and the application of software engineering principles to practical scenarios. The teaching strategy combines theoretical lectures with extensive practical workshops, supported by guided independent study and formative feedback opportunities.

Assessment

This module will be assessed by a UML modelling and design patterns portfolio (40%) and system implementation and testing (60%).

30 credits

This module develops your ability to design, implement, and manage modern data systems within cloud environments. It builds on your prior knowledge of software engineering and using data to introduce the key principles, tools, and practices that underpin scalable, secure, and sustainable data engineering in the cloud.

You will learn how to model, ingest, and process data using distributed and virtualised resources; orchestrate and automate data workflows; and integrate analytics or machine learning components to generate actionable insights.

Core topics include:

• Introduction to Cloud and Data Engineering
• Cloud infrastructure and services for data engineering
• Data modelling, ingestion, and integration techniques
• Data transformation and processing in the cloud
• Data storage solutions in cloud and on-premises environments
• Pipeline orchestration and automation
• Cloud data security, privacy, and governance
• Integrating machine learning into cloud workflows
• Cloud monitoring and performance optimization
• Designing and presenting end-to-end cloud data solutions

Teaching and learning

Teaching combines seminars and practical lab sessions, and project-based group work, encouraging you to solve real-world problems and collaborate effectively.

Seminars will introduce key concepts, frameworks, and case studies, while labs will focus on hands-on development and collaborative problem-solving teams.

Independent study, guided tasks, and formative feedback points ensure that you are supported in applying theory to authentic technical challenges.

Assessment

This module will be assessed by a design and implementation of a cloud data pipeline, group project (40%) and an integrated cloud data solution, group project (60%).

30 credits

This module is designed to develop the professional skills and mindset needed to succeed in technology-related careers, alongside enhancing your research and academic skills.

Taking key emerging technologies and current professional issues within your chosen pathway as a starting point, you will define and develop your own area for enquiry. You will be supported to systematically find and analyse academic and industry research publications and review existing technical solutions relevant to your area.

You will return to key topics in professional practice, including project lifecycle management, professional frameworks, agile and traditional methodologies, technical communication, ethical and legal considerations with deeper understanding, and apply these to your own project.  You’ll also engage with global perspectives and inclusive practices relevant to your discipline.

Teaching and learning

Your learning will be active and applied. In class, you’ll participate in seminars, lab-based workshops, and project supervision sessions. Outside the classroom, you’ll conduct independent research, collaborate with peers, and use industry-standard tools such as Git, Trello, and JIRA to manage your work.  

Each week includes a four-hour teaching session, structured as:

• Interactive keynote discussion - introducing key concepts, frameworks, and professional contexts.
• Guided workshop activities - hands-on practice with tools, methods, and scenarios; individual and collaborative problem-solving.

You will begin to engage with outside stakeholders relevant to your project and apply your knowledge and skills in user research and requirements gathering.

Assessment

This module will be assessed through coursework, where you will prepare a literature review on a chosen topic (50%) and poster project proposal with a Q&A session (50%).

30 credits

This module introduces you to the fundamental principles, algorithms, and practices of Machine Learning (ML), the field where systems learn from data to make predictions, whilst integrating the art and science of Data Visualisation to interpret and communicate insights effectively.

Through a balanced blend of theory and practice, you will explore the end-to-end ML workflow: from data pre-processing (cleaning and organising data) and model development to evaluation, interpretation, and visual storytelling.

You will gain practical experience with a wide range of ML techniques including regression (predicting continuous values) and classification models (predicting categories), neural networks (systems modelled after the human brain), clustering, unsupervised learning (finding patterns in unlabelled data), reinforcement learning (systems learning through trial and error), and deep learning (advanced neural networks).

Additionally, you will develop expertise in high-dimensional data visualisation and Explainable AI (XAI), enabling you to make complex models interpretable and their outputs accessible to diverse audiences.

Teaching and learning

Teaching combines seminars and practical laboratory sessions with project-based group work, encouraging you to solve real-world problems and collaborate effectively.

Seminars will introduce key concepts, algorithms, and example datasets, whilst laboratories will focus on hands-on model development and collaborative problem-solving in teams. This approach ensures you gain both theoretical understanding and practical implementation skills essential for professional machine learning practice.

Independent study, guided tasks, and formative feedback points ensure that you are supported in applying theory to authentic technical challenges. All resources and recordings will be accessible online, and regular guidance and peer collaboration will help you build confidence and achieve your full potential.

Assessment

This module will be assessed by a machine learning model design and visual evaluation group project (40%) and an integrated machine learning and visual analytics group project (60%).

30 credits

The Capstone Project provides you with the essential opportunity to deeply explore a subject of high personal interest, situated within the context of your overall programme of study.

You are expected to apply and synthesise your professional practice and research capabilities throughout this project. By bringing these skills together, you will conduct a substantial investigation that extends and demonstrates your practical and academic knowledge. Upon completion, you will produce a significant technical artifact alongside a detailed report.

Building on the Professional Practice in Technology module, you will be able to critically engage with current literature and established research methodologies. Using the knowledge and feedback gained from this module, you will be equipped to develop your research question or problem statement. This work will lead you to systematically evaluate relevant sources, creating a strong foundation for your investigation.

Implementing knowledge gained from the earlier module will enable you to strengthen your methodological approach and demonstrate advanced research and problem-solving skills in your capstone project. You will be able to design and implement an evidence-based solution that addresses your research question and stated objectives. The project culminates in a critical evaluation and reflection on the impact of your solution in relation to your original research goals.

You will be assigned a named individual supervisor who will provide expert guidance throughout your project. While your supervisor is key, you are strongly encouraged to access the full range of academic and research support systems available across the university to enhance your work.

Collaboration is often a feature of the Capstone Project: you may either work with external stakeholders who provide a real-world project brief, or you may have the chance to work alongside an academic, contributing directly to their ongoing research. This project is not undertaken in isolation; peer support is fundamental, and your Capstone Project is developed within a strong, supportive learning community of both staff and fellow students.

Teaching and learning

The module is delivered via three modes of study:

• On-campus sessions that help you conform to the necessary timeline and project requirements
• One-to-one feedback from a named supervisor
• Independent study

The on-campus sessions provide practical activities which directly contribute to the students’ project deliverables.

Peer support via on-campus session activities and group supervisory meetings is strongly encouraged and rewarded via digital badges in Moodle.

Assessment

This module will be assessed by a feedback-feedforward and Q&A session (30%) and a report and artefact (70%).

30 credits

You will develop your core academic and integrated English language skills of speaking, listening, reading and writing. You will become familiar with key academic skills and concepts, such as referencing methods and awareness of academic integrity and tone. You will apply these skills and knowledge to both broad topics and also your chosen subject pathway.

Teaching and learning

You will be required to actively engage in on-campus learning for up to 10 hours a week.

You will be taught through a full range of teaching and learning methods, which include lectures, seminars, workshops, discussion groups, group directed tasks and presentations. This will enable you to learn from your peers and tutors in both structured and information settings.

You will be encouraged to think creatively about your approach to learning and discussions with your peers. You will also have access to recordings, resources, links and signposting through Moodle to enrich your learning.

Assessment

You will be assessed through group and individual presentations, comparative and reflective essays, multiple choice exams, coursework and reports, oral exams, portfolios, case studies and blogs.

30 credits

You will develop your core academic and integrated English language skills of speaking, listening, reading and writing. You will become familiar with key academic skills and concepts, such as referencing methods and awareness of academic integrity and tone. You will apply these skills and knowledge to both broad topics and also your chosen subject pathway.

Teaching and Learning

You will be required to actively engage in on-campus learning for up to 10 hours a week.

You will be taught through a full range of teaching and learning methods, which include lectures, seminars, workshops, discussion groups, group directed tasks and presentations. This will enable you to learn from your peers and tutors in both structured and information settings.

You will be encouraged to think creatively about your approach to learning and discussions with your peers. You will also have access to recordings, resources, links and signposting through Moodle to enrich your learning.

Assessment

You will be assessed through group and individual presentations, comparative and reflective essays, multiple choice exams, coursework and reports, oral exams, portfolios, case studies and blogs. 

30 credits

You will develop your research, numeracy and information technology skills. You will investigate the difference between primary and secondary research, conduct your own research project and demonstrate your findings through data analysis. You will also develop your awareness of equality, diversion and inclusion in the UK, through a real-world issue; discrimination in the workplace.

Teaching and learning

You will be required to actively engage in on-campus learning for up to 10 hours a week.

You will be taught through a full range of teaching and learning methods, which include lectures, seminars, workshops, discussion groups, group directed tasks and presentations. This will enable you to learn from your peers and tutors in both structured and information settings.

You will be encouraged to think creatively about your approach to learning and discussions with your peers. You will also have access to recordings, resources, links and signposting through Moodle to enrich your learning.

Assessment

You will be assessed through group and individual presentations, comparative and reflective essays, multiple choice exams, coursework and reports, oral exams, portfolios, case studies and blogs. 

30 credits

You will be taught about how the use of computing software impacts the environment from day-to-day activity through use of business and everyday jobs. This is through the excessive use of computing software as well as the changing way that society operates. You will be encouraged to engage in debates and discussions regarding energy consumption in the use of computing software, in terms of the amount of use as well as the type of materials utilised. You will be encouraged to look at responsible innovation and software impact and the long-term effects this may have.

You will look at how the increased use of IT and computing has contributed to the significant development of globalisation in the interconnectivity and internationalisation of the job market. Discussions on how globalisation has contributed to the international development of education, technology development and the emerging market economies through operating systems and internet operations. This is also linked to their assignment in the international platform of Linked In in developing their e-portfolio.

Teaching and learning

The teaching delivery for each module consists of one, one-three-hour lecture and one, two-hour lab session each week with pair and group work.

There will also be a 30-minute weekly virtual task and multiple-choice quizzes.

Assessment

This module will be assessed using a multiple-choice test and online portfolio.

40% - multiple choice test, which will take place online during a class.

60% - online portfolio, in this written task you will create a blog post, which reports on an individual data research and processing project. You will also create a LinkedIn profile and subject weekly learning journal entries.

30 credits

This module focuses on how to solve problems via program development. The module initially builds your understanding of logic, before teaching you how to develop algorithms. The latter half of the module focuses on programming using a Visual Programming Language (VPL) such as Android App Inventor or Google’s Blockly. The module ends with a discussion on the limits of computation, including data representation and physical limitations.

You will be taught about how the use of computing software impacts the environment from day-to-day activity through use of business and everyday jobs. This is through the excessive use of computing software as well as the changing way that society operates. You will be encouraged to engage in debates and discussions regarding energy consumption in the use of computing software, in terms of amount of use as well as type of materials utilised. You will look at responsible innovation and software impact and the long-term effects this may have.

The module explores how advancements in computing and IT have accelerated globalisation by enhancing interconnectivity, transforming industries, and expanding access to information. You will examine the role of computational thinking and problem-solving in a global context, including the impact of software development, and automation on international markets. Discussions will focus on how programming and algorithm design contribute to technological advancements, remote collaboration, and the evolving global job market. Additionally, you will be able to engage with international computing standards and digital platforms, such as LinkedIn, to develop your profile and understand the significance of global networking in the technology sector.

Teaching and learning

The teaching delivery for each module consists of one, one-three-hour lecture and one, two-hour lab session each week with pair and group work.

There will also be a 30-minute weekly virtual task and multiple-choice quizzes.

Assessment

This module will be assessed using programming coursework and a class test.

60% - coursework, you will design a simple algorithm to solve a problem using flowcharts and pseudocode.

40% - class test, you will construct a program to solve using a Visual Programming Language.