Computer Science (Top-up)

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%).