This module introduces technologies needed to create modern web-based applications.  It will develop your knowledge and practice skills in creating dynamic websites, that are underpin by databases, using standardised web technologies. 

You’ll study topics such as: 

• HTML 

• CSS 

• Javascript 

• PHP 

• SQL 

This module will introduce concepts of computer architecture,  discrete mathematics and numerical skills related to concepts for application in computer science. It will introduce the fundamental principles and features that underpin modern computing systems design and describe how the execution of software is influenced by the processor architecture being used.  

You’ll study topics such as: 

• Sets, relations, and functions 

• Propositional and predicate logic 

• Cardinality, combinatorics, recurrence, and modular arithmetic 

• Proof techniques: structure, induction, counterexamples, contradiction 

• Graphs and trees 

• Discrete probability 

• Data organisation: data structures, e.g., arrays, lists and trees 

• Matrices 

• Mathematics of cryptography 

• Hexadecimal and binary number systems 

• Floating point and other number systems 

• Logic gates and circuits 

• Boolean algebra 

• Machine code and programming in a low-level assembler language 

• The fetch-execute cycle 

This module introduces computer programming. It will develop your understanding of practical programming concepts and their deployment in a mainstream object-oriented programming language through the creation of code and applications. 

You’ll study topics such as: 

• Problem solving, top-down design and functional decomposition 

• The development process - specification, design, implementation, testing. 

• Variables, data types, and data structures 

• Algorithms and control structures 

• Object-oriented programming 

• Program quality 

• Software tools and integrated development environments (IDEs) 

This module uses real projects for you to apply and continue to develop your software development knowledge against.  It will support the development of your collaborative skills in a software development environment alongside relevant Legal, Social, Ethical and Professional Issues (LSEPI) and their impact within the IT and software industry. 

You’ll study topics such as: 

• User requirements & user stories 

• Acceptance testing 

• Software prototyping and fidelity of prototypes 

• Software development process and lifecycles 

• Use case modelling 

• Database design 

• Object-oriented design 

• User interface design 

• Legal, Social, Ethical and Professional issues in IT 

• Software testing 

• Sustainable development and deployment 

• Data security 

This module will continue to build your programming knowledge, looking at object-oriented programming in a modern programming language such as C/C++.  It will introduce manual dynamic memory management and discuss heap and stack memory alongside profiling.  You will practice more advanced aspects of programming languages and develop an understanding of design patterns and standard software libraries. 

You’ll study topics such as: 

• Object-oriented principles, concepts and design: encapsulation, inheritance, polymorphism 

• Abstract and interface classes 

• Dynamic memory management 

• Memory pointers and references 

• Memory profiling 

• Standard template libraries (STLs) 

• Operator overloading 

• Templates / generics 

• Event-based coding 

• Design patterns 

• Tool support for OO development  

• Version control software  

Aims: 

This module will impart key concepts in computer science to students and to further develop important skills in computer-based problem solving and data manipulation.  

Indicative Content  

• Introduction to algorithms (role and importance of algorithms in computer science). Concurrent, and parallel algorithms 

• Sorting algorithms (merge sort, heap sort, quick sort) 

• Searching algorithms 

• Linked lists (Singly linked lists; double linked lists) 

• Stacks and Queues 

• Recursion  

• Tree structures (creating, searching, traversing, merging). 

• Graph structures (traversals, activity networks, critical paths, shortest paths) 

• The theory of algorithms (Big-O notation, Computability; Turing machines). 

• Complexity (computational and control flow). 

• Optimisation problems (Travelling Salesman Problem, Bin Packing). 

• Optimisation search methods (Genetic algorithm, Hill Climbing, Simulated Annealing, Tabu search algorithm, Iterated Local Search).  

This module delivers knowledge and practical skills of machine learning algorithms for artificial intelligence applications. It will present the key mathematical principles and concepts required to create machine learning algorithms through data-driven approaches. You will gain practical experience while designing, implementing, and evaluating machine learning systems to solve real-world artificial intelligence problems. 

You’ll study topics such as: 

• Data-driven approaches to machine learning 

• Linear regression and gradient descent  

• Regression and classification problems  

• Machine learning algorithms: decision trees, support vector machines, ensemble learning and k-mean/mean-shift clustering 

• Data pre-processing, cleansing and feature extraction  

• Evaluating machine learning system by using cross-validation, confusion matrix and receiver operating characteristic 

This module provides a platform for you to work collaboratively in small teams to build complex software applications relating to industry.  You’ll typically work with external clients on the projects, where you will also continue to develop your professional practice and profile, that can be used to support placement opportunities. 

You’ll study topics such as: 

• Modern software development methodologies 

• Effectively team working  

• Version control tools  

• Architecture of CRUDing applications  

• Building secure APIs  

• Testing strategies for software development  

• Cloud deployment technologies  

• Professional profile: CVs, covering letters, portfolio, and reflective practice 

• Building secure and sustainable systems 

• Accessible and culturally appropriate software 

This module expands your experience of designing and creating gameplay prototypes for Windows-based PCs, using high-level commercial engines such as Unity and Unreal. You’ll build on your existing commercial engine skills to develop a gameplay prototype for a mobile platform.  

You’ll study topics such as: 

• Game development in a scripting language such as Lua and C# 

• Researching and applying common design paradigms to mobile gaming 

• Advanced techniques of a commercial games engine 

• Particle systems, shader programming, post-processing effects and networking 

• Monetisation of the mobile gaming platforms 

• Common industry practices to mobile game financing 

This module emphasizes the necessity of incorporating ethical design principles into AI and data science projects, highlighting the importance of fairness, transparency, and accountability. Learners will explore data ethics, including privacy, consent, and bias, ensuring responsible data handling throughout its lifecycle. The module also covers data security, focusing on protecting data from unauthorized access and breaches, and maintaining integrity and confidentiality.  

Students will engage with topics of data protection, and fairness in AI by developing collaborative research, to identify the risks and develop strategies for mitigation. Students will be challenged to discuss issues surrounding bias and inequality in context of data science to be able to thrive as reflective practitioners. 

INDICATIVE CONTENT 

The module will cover relevant regulations, governance frameworks, and standards essential for ethically aligned design, highlighting their connection to various stages of the data science lifecycle. Students will engage with real case scenarios to apply this knowledge and be challenged in applying these techniques and tools in areas including data anonymization, debiasing, and fairness testing.  

By integrating these principles learners will be able to thrive and collaborate in the design and implementation of innovative, ethically sound, and secure AI and data science projects, building trust and credibility in AI systems. 

You’ll study topics such as:  

• Data Protection and Privacy Regulations  

• Technologies for anonymizing data: k-anonymity, and differential privacy. 

• Risk and Accountability Regulations  

• Professional Ethics and Codes of Conduct  

• Ethical practices of data handling including issues of inequality, bias, and fairness in domain 

• Conflicts of Interest and Ethical Decision Making Client Relationships, Communication, and Complaints Handling.  

• Strategies to reduce race and gender discrimination from automated systems 

The aim of this module is to give you the opportunity to apply the principles of good design, usability, and security to the development of software. It does so by exploring a case study that forms the starting point for discovery, discussion and development. In adopting a practical approach, different design techniques and architectural styles can be used to develop solutions that are then evaluated against industry-standard criteria using both qualitative and quantitative techniques. 

Indicative Content 

• Gathering requirements 

• User-Centred Design (including aspects of inclusive design with a particular focus on assistive technology). 

• Designing a solution architecture 

• Selecting and implementing design patterns. 

• Understanding anti-patterns and code smells. 

• Evaluating software architecture and quality characteristics. 

• Understanding the role of measurement and modelling in the design of systems. 

• The relationship between frameworks, design patterns and architectures. 

• Implementation of software using frameworks. 

• Software and user testing. 

• Testing the security of a system 

• The importance of understanding of culture and inclusivity in the design of systems