BSc Single Honours Computer Science

This module will provide students with the opportunity to be proficient with tools and techniques used to create content accessed across a variety of mobile, tablet and desktop devices reliant on current front-end web design technologies. Students will also recognise appropriate accessibility standards and apply compliance guidelines to interactive websites. Students will develop web design skills for creating usable and interactive front-end web pages based on the principles of Human-Computer Interaction (HCI). Students will be encouraged to explore the requirements of organisations and interactive front-end development. Students will review appropriate literature on website design to gain understandings of the planning and implementation process and identify areas for further development or study.

In this module, students will develop an understanding of the ethical, professional, sustainable and legal issues relating to computer science. Students will develop critical investigative skills in applying ethical theories to novel and emerging technological outcomes. . This module will provide students with the opportunity to identify and reflect on their strengths and weaknesses and to consider requirements for their future career in computer science. Development of problem-solving skills using number theory, propositional and predicate logic will enable understanding of a range of computer science concepts. Students will participate in critical reading, writing and develop their referencing techniques with emphasis on evaluating reliability and validity of sources.

This module provides the first exposure to programming in this undergraduate degree programme, and for some students their first encounter with programming at all. It introduces problem-solving and structured, procedural and function-oriented programming. Emphasis is placed on designing, developing and deploying correct, efficient, maintainable and scalable programs.

Introduction to problem solving, a brief review of Von-Neumann architecture, introduction to programming, role of compiler and linker, introduction to functions & algorithms, basic data types and variables, input/output constructs, arithmetic, comparison and logical operators, conditional statements and repetitive statements with understanding of execution flow, translation of counting, permutations and combinations into programs, introduction to modular programming, function definition and calling, stack rolling and unrolling, string and string operations, pointers/references, memory allocation and file handling.

The focus of this module is on developing knowledge, skills and understanding as they demonstrate mastery of software development. The module is based on several example programmes and emphasis is placed on practical work with the aim of ensuring that theory covered in lectures is reinforced by practical programming tasks.

This module teaches object-oriented programming to those who have learnt fundamental programming concepts. The main concepts discussed are: Objects, Data Abstraction, Data Encapsulation, Polymorphism, and Inheritance with practical implementation using contemporary programming language constructs. The aim is to familiarise students to the object-oriented programming paradigm, analysis and software development. The programming skills are further enhanced by teaching data structures and algorithms underpinning efficient manipulation, storage and retrieval of data. Students are exposed to the concepts of time and space complexity of computer programs.

The module introduces object-oriented design, history and advantages of the paradigm, classes, objects, data encapsulation, constructors, destructors, access modifiers, function overloading, operator overloading, association of classes: composition, aggregation and inheritance; polymorphism, abstract classes and interfaces, generic programming concepts, data and object serialization using object streams and exception handling. The module further familiarizes students with concepts of creating, storing, retrieving, ordering, and manipulation of abstract data structures and the basics of analysis of algorithms and growth of functions. Concepts of linked lists, stacks, queues, trees and graphs are discussed along with recursion and divide and conquer algorithms. Techniques of sorting (selection, insertion, merge, quick, bubble, heap and radix etc.), priority queues, sorted linked lists, binary search, traversal of trees and graphs, greedy algorithms, spanning trees, hashing and indexing are discussed and practical implementation of these using a contemporary programming language is demonstrated.

The focus of this module is on developing algorithmic thinking, object-oriented programming knowledge, skills and understanding based on medium complexity programmes. Emphasis is placed on practical work with the aim of ensuring that theory covered in lectures is reinforced by practical programming tasks.

This module will introduce students to concepts involved in the fundamentals of computer networking and security. The emphasis is on the design of computer networks and fundamentals of cyber security.

In this module, students develop the knowledge, skills and understanding required to comprehend computer models, methodologies, structures, number representation, security, Boolean Algebra, logic gates and their role in basic computer systems. Knowledge of fundamentals of computer architecture is becoming increasingly important in business and finance, and are applicable to problems which have been considered mainstream computing.

An operating system is typically the lowest layer of software in a computer. It provides an abstracted interface so that applications can run on diverse hardware without modification and it provides security which prevents misbehaving software from ‘crashing’ the hardware or disturbing other tasks which may be running simultaneously. This module provides an introduction to the major principles of implementing an operating system and ensuring that these are secure.These fundamentals cover the principles and structures of network addressing, , system architecture design, transmission of media, security and operations.

The module will give students the opportunity to learn the fundamentals of database design. They will investigate the structure of data within a relational database, interact with, and protect the data within the database. Students will develop practical experience of problem analysis, especially concepts of data modelling, sets, relational theory and relational algebra. Students will develop database implementation skills for optimisation using structured data, held in relational databases, accessed via SQL and explore the data storage requirements of on-line businesses, companies and organisations.

This module aims to teach students the concepts, skills and knowledge required to develop mobile applications. Students will appraise the tools, processes and techniques used to plan, design and deploy mobile applications. This module provides the students with the opportunity to evaluate the technical and management dimensions of mobile applications development. The students will critically review relevant guidelines, principles and research to identify different application development strategies and build on existing software development skills to create a mobile application for a given context.

Module Summary:

The module aims to develop core competencies in the fields of secure software concepts, secure software requirements, design, implementation, coding and testing. Nowadays, almost all the software dependent appliances, devices, business services and industries are susceptible and vulnerable to hostile cyber attacks intended to exploit security weaknesses in operating systems, mobile apps, web apps, databases and web servers.

This module teaches a systematic method applied throughout the development lifecycle that places security at the centre of product development with focus on people, processes, and technology components of software, networks, and host defences to prevent, mitigate and recover from malicious interventions. Topics of discussion include security concepts in the SDLC, risk management, best practices (e.g., NIST and OWASP etc.), trusted computing, secure software design, known vulnerabilities and malware attacks, SQL Injection and legal and ethical issues.

The module aims to develop understanding of the role and interaction of policies, laws, procedures, management issues, and technical issues in protecting information resources including confidentiality, integrity and availability in computer systems. An introduction to risks and attack models for information systems and cyber-physical systems is presented along with ISO/IEC 27001 guidance for implementing and maintaining secure information systems with proactive approaches to assure resilience.

This module introduces concept, design, functions, architecture, components and applications of secure information systems (IS), information security policies and procedures, IS system planning and administration, asset management, organizational and human security, cyber security management concepts, NIST cyber security framework, enterprise roles and structures, security plans and policies, contingency planning, laws and regulatory requirements, security standards and controls, risk management process, NIST risk management framework, security metrics, physical security and environmental events, contingency planning, security education and the ISO/IEC 27001 compliance, training, and awareness.

Machine Learning, is a subset of Artificial Intelligence, is concerned with creating learning models that allow a computer to exhibit behaviour that would normally require a human to do. Typical applications include: computer vision, speech recognition, and intelligent robots. The learning models come in various forms, such as parametric and non-parametric and probability distributions. The emphasis is on creating these models automatically from data provided and for them to act without necessarily being explicitly programmed, for example, creating a face recognition system from a data set of facial images. In the past decade, machine learning has given society effective web search, self-driving cars, practical speech recognition, and an understanding of the human genome.

In this module, students will learn about the most effective machine learning techniques, and gain practice in implementing them successfully. Additionally, students will learn about the theoretical underpinnings of machine learning. Topics covered include: concepts of statistics & probability, supervised learning, unsupervised learning, matrix operations, backpropagation using partial derivatives, eigenvalues and eigenvectors, support vector machines and best practices in machine learning. Case studies and applications will be used to illustrate best practice in machine learning.

The Cyber Security module introduces students to the concepts required to understand and to work with computer security. These security concepts include, for example: historical background, security and security threats, practical measures (such as identification, authentication), security models, cryptography, network security). The second part of the module provides students with the opportunity to explore computer system security via hands-on activities. Current issues relating to cyber security will be discussed and possible solutions will be investigated through practical and hands-on labs Students will critically reflect, design, and develop their learning into a security-centric project to understand historical and novel cyber security threats and learn how to defend against these through practical experience.

This module enables students to investigate the scope and challenges of Human Computer Interaction (HCI) and interaction design. They will develop an understanding of HCI concepts and apply HCI methodologies to augmented reality and tangible user interfaces. Students will investigate current user-centred design research and participate in the process of designing a high-fidelity prototype that meets user requirements. They will explain, discuss and apply the underlying principles in HCI, including human physiological, psychological and technical aspects. Usability evaluation methods will be deployed to assess potential design options and trade-offs.

This module aims to provide students the necessary theoretical and practical skills to understand, design and develop statistical machine learning models for pattern recognition problems such as object detection, recognition, segmentation and texture analysis. Students will be able to apply various techniques for real-world applications such as person identification, audio processing, speech recognition, image analyses and motion estimation.

In this module students will learn one of the fundamental areas of artificial intelligence – representing knowledge about the world. It explores how cognitive modelling enables development of an automated expert system to make intelligent decisions analogous with human deductive reasoning. Encompassing information using semantics, predicates, propositions and rules is imperative in application domains such as autonomous vehicles & robotics, medical diagnostics, financial technologies and interactive natural language tools.

The module will introduce existing frameworks, representation using well-formed formulae, ontologies, planning and reasoning that can be used to support the task of constructing suitable representations for decision making applications within artificial intelligence. Concepts of propositional logic, first-order logic, description logic, reasoning using description logic, forward and backward chaining in inference engines, semantic networks, ontologies, logical agents, constraints, planning, rule-based knowledge, reasoning under uncertainty, Bayesian belief networks, fuzzy logic and inference, Markov Models and Explainable AI will be discussed.

The focus is to explore how symbolic knowledge representation and relevant reasoning support the task of developing artificial intelligence solutions in a high-level, logical and declarative programming language.

This module aims to introduce students to the paradigm of cloud computing, where technology-related functions are provided as a service to users in order to perform resource-intensive tasks such as data analysis and machine learning. They will learn formal methods and techniques for designing and implementing advanced cloud-based applications. Students will have an appreciation for Artificial Intelligence and Semantic Web research related to cloud computing. They will investigate big data computing using cloud-based technologies and apply the skills and knowledge gained to design, develop and deploy an interactive data-driven cloud-based application to solve programming problems.

This module teaches students the concepts and processes of vulnerability assessment and ethical hacking covering software, networks, and Web applications. Students will use state-of-the-art tools to identify real-world vulnerability issues in modern software and the protection mechanisms found in operating systems. Students will learn how to detect, patch, and develop extensions of protection mechanisms to mitigate attacks and improve security features of software. This module introduces the core methodological approaches to application vulnerability assessment, reverse engineering, and ethical hacking. Students will engage with novel technologies to attack or ‘penetration test’ vulnerable systems and applications. Students will then learn how to defend against such attacks using a mix of academic and industry-focused techniques.

This module covers need for security assessments, classifying vulnerabilities: software, penetration testing, memory corruption, cross site scripting, fundamentals of the C to understand code analysis, reverse engineering of software, simulated attacks, security assessment, intercepting data, and privilege escalation.

The final project and critical evaluation will be structured to assess the knowledge and understanding by means appropriate to individual students, though all approaches will lead to the production of a significant piece of work that involves the demonstration of advanced practice in computer science. All approaches will be accompanied by a critical self- evaluation of the outcomes achieved. Students would integrate concepts and skills learned throughout the degree course to substantial open-ended problems. The module will increase students understanding of applied, investigational or theoretical approaches to real word problems.

This module enables students to build on their cyber security knowledge using digital evidence collection, preservation, and analysis. The module explores the tools and techniques used to investigate data, for example; data recovery, network forensics, and malware analysis. The module also explores the legal and ethical considerations in cyber forensics, ensuring students understand the importance of confidentiality, integrity, and availability. By the end of the module, students will have an awareness of cyber forensics approaches and consider how these could be applied in practice. The module encourages students to understand how digital data can be used in cyber security.

This module covers the investigation and forensics of digital technologies, including; security mechanisms, cryptography, encryption, hash functions, digital signatures, key management, authentication and access control; software security, digital forensics tools, malware, firewalls, intrusion detection, security policies, law and ethics in information security, privacy and anonymity of data.

Low-cost network devices add eyes, ears or sensors and arms, legs or actuators to the Internet. These devices are then connected to a computer, either physically, via wireless or through the cloud, communicating live data. This computer will then utilise machine or deep learning in the cloud. This field of study is referred to as the Internet of Things (IoT).

This module will cover the context and the history of the IoT, the hardware, communications protocols and security systems it relies on, and the cloud-side analytics that makes sense of the data produced. It will provide practical hands-on experience of common IoT devices (sensors, actuators, microcontrollers) and examine a range of commercial platforms.

Students will work with a Wi-Fi microcomputer and will program live IoT applications using that device. Students will have the opportunity to produce their own IoT device with a range of hardware and capabilities. Emphasis is placed on the versatility of components that can be integrated into such systems, as well as the diversity of data types produced by IoT edge nodes. Development of IoT applications also requires communication between different level (layers) of IoT architecture with the associated security and privacy issues.

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain.

This module will further build on the concepts of machine learning, neural networks and artificial intelligence and will introduce advanced topics in neural networks, convolutional and recurrent network structures, Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged on graphical processing units (GPUs) using contemporary architectures and frameworks. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations, and implementation of algorithms using programming languages.

In this module, students will learn about the most effective machine learning techniques, and gain practice in implementing them successfully. Additionally, students will learn about the theoretical underpinnings of machine learning. Topics covered include: supervised learning, unsupervised learning and best practices in machine learning. Case studies and applications will be used to illustrate best practice in deep learning.

This module will provide students opportunity to understand and apply computational techniques to analyse and synthesize natural language and speech. An interdisciplinary bridging of information retrieval and machine learning will provide necessary skills to develop applications capable of comprehending, manipulating and generating natural languages similar to Large Language Models.