BSc (Hons) in
Computer Science

This module introduces computational thinking, programming or information technology knowledge. It covers a wide range of topics related to basic mathematics and logic, computer organisation and architecture, computational thinking and operation systems basics. Common file formats and basic security issues related to data are also introduced.

Assessment: There are two assessment components: a group project and an in-class test.

The module introduces the basics of programming – variables, data types, conditional branching, loops, collections, and functions. Emphasis is placed on practical experience with a modern programming language. You’ll learn to analyse small-scale problems, design solutions using algorithmic and mathematical techniques, and implement them through practical programming. Hands-on exercises and labs will sharpen your problem-solving skills while reinforcing key programming concepts.

Assessment: Assessment for this module consists of two components: an In-Class PC Lab Test and a Practical Coursework Project. The PC Lab Test accounts for 40% of the final grade and is designed to evaluate students’ understanding of core concepts as well as their ability to apply technical knowledge under timed conditions. The Practical Coursework, which makes up 60% of the final mark, requires students to complete an individual project that demonstrates practical problem-solving, creativity, and the application of theoretical knowledge to real-world scenarios.

The module is designed to teach students structures and schemes, which allow them to write a programme to efficiently manipulate, store, and retrieve data. Study of the basic notions of the design of algorithms and the underlying data structures. Students are exposed to the concepts of time and space complexity of computer programmes.

Assessment: The module has two graded assessments. Assessment 1 is a coursework project which will allow evaluation of students’ comprehension of the theoretical aspects of the module. Assessment 2 is a PC-Lab test which will evaluate student’s Algorithm and Data Structure knowledge, concepts, and implementation.

The emphasis of the module is on the application of statistical methods to tackle real-world, data rich problems in economics, finance, business and academia. Attention will focus on the calculation and interpretation of tables and results and the appropriate method to approach various aspects of real-world situations. Treatment is at an elementary mathematical level (basic calculus). Concepts of probability, statistical bias, randomness, random variables, sampling, statistical inference, basic data exploration and visualization techniques, data analytical methods, bivariate and multivariate analysis are introduced.

Assessment: Assessment for this module consists of two tasks. Assessment 1 is an in-class test that serves as a summative assessment and evaluates students’ knowledge of the fundamental concepts and techniques introduced in the first half of the module. Assessment 2 is the final exam, conducted at the end of the semester. This assessment covers material from the entire module and provides a comprehensive evaluation of students’ understanding and ability to apply statistical concepts and methods in a range of contexts.

The module introduces the basics and principles of graphic design. It mainly focuses on developing practical skills in digital typesetting, image making, composition, and layout, which students can apply to different business and professional contexts. Students will learn to use industry standard graphics applications to create their visual designs.

Assessment: Assessment for this module consists of three pieces of coursework. Through these projects, you’ll get the chance to put theory into practice, sharpen your problem-solving skills, and show that you can deliver professional, well-structured work.

This module is a general introduction to Artificial Intelligence (AI). It will introduce diverse topics of AI to students from any background. It is structured to offer students essentially practical skills in AI and its subfields that have made significant impact, including but not limited to: Search problems, Knowledge Base Systems, Fuzzy Logic, Neural Networks, Evolutionary Computation, Computer Vision, Generative AI, Natural Language Processing, and machine learning.

Assessment: Assessment for this module consists of two components. The first is a Group Project, which accounts for 50% of the final grade. The second component is a Lab Test, also weighted at 50%. This in-class test evaluates students’ individual understanding of core concepts and their ability to apply technical knowledge under timed conditions.

This module introduces students to the methods and processes involved in creating 3D models and environments, and renderings. The key areas of modelling, texturing, lighting and rendering will be studied, and various modes of output demonstrated. Different applications of Computer-Generated Imagery (CGI), such as motion graphics, character animation and virtual environments are discussed.

Assessment: The module will be assessed by means of coursework and PC lab in-class test. The Practical coursework aims to assess students’ ability to analyse the theoretical frame of implementation of CGI, while the in-class test assesses the student’s ability to create a 3D asset.

The module provides a practical introduction to the core concepts used in the design and implementation of database information systems. The module provides a solid approach to database theory and its practical perception and investigates the fundamental issues related to Conceptual Data Modelling, Logical Data Models, and Relational Information Retrieval.

Assessment: The module will be assessed by a Project and a Computer Lab Test. The project is practical work that will require students to develop a database; a Computer Lab Test will be a practical assignment conducted in a PC lab.

This module introduces the Object-Oriented Programming paradigm. The students will learn the difference between Procedural Programming and Object-Oriented Programming. They will also be introduced to the Object-Oriented Programming principles, design patterns and UML class diagrams to be able to visualize system components and build them based on the design.

Assessment: Assessment for this module is divided into two parts. The first component is an in-class Programming Test, which carries a weighting of 50%. The second component is a Coursework Practical, also weighted at 50%. This coursework task requires students to develop and present an individual solution to a practical problem, allowing them to showcase problem-solving skills, creativity, and the application of theoretical knowledge to practice.

The module will provide an overview of modern operating systems and their components. Students will learn about CPU, memory and storage management, processes and threads, file systems, file types and access modes. Moreover, the module will discuss containers and virtualisation.

Assessment: The module will be assessed through a case study project and a final exam.

This module introduces end-to-end full-stack web development, focusing on building web applications. Students will learn essential frontend and back-end technologies, database design, security practices, and deployment. The module emphasizes practical learning aligned with real-world business needs.

Assessment: The first is a Business Case Report, which accounts for 20% of the final grade. The business case report is designed to assess students' ability to critically analyze a business scenario and translate it into a coherent and viable plan for web application development. The second component is a Practical Development Portfolio, weighted at 80%. The assessment ensures that students can apply their knowledge in a real-world context, creating functional web applications from start to finish.

The module introduces the basics of game development, starting from the history of the game industry, genres, main metrics, design. It focuses on key concepts of game storytelling and development, applicability of promotion techniques and revenue models for the game as a software product.

Assessment: The module will be assessed by means of a portfolio consisting of report and game prototype. The report forms the business proposal of the game by discussing the game storytelling, design, monetization and promotion of the game. The game prototype will enable students to apply concepts and ideas discussed in the report in the actual development of the game prototype.

Students learn how to reorganize data files of different formats (google sheets, Excel, SQL, xml, etc.) into a single consistent form and visualize the data with Dynamic slicers. The dashboarding techniques for online visualization and handling of cognitive load will be also handled. Working individually and in a team is practiced in this module.

Assessment: The module will be assessed through two components. The first is a Group Project, which contributes 50% to the final grade. This assessment requires students to work collaboratively to investigate a problem, develop solutions, and present their findings, thereby testing their teamwork, research, and communication skills. The second component is a Lab Test, also weighted at 50%.

The module introduces programming in the context of mobile applications. The students will learn about the basic principles of mobile applications development, including internal architecture, interface building and database connection.

Assessment: The module will be assessed by means of PC laboratory test and a mobile application (coursework practical). The test aims to assess students' skills in using IDE, writing basic UI markup code and understanding the structure of main configuration files and classes of a mobile project. Mobile application is worth 70% of the overall mark, and assess students' ability to apply their knowledge to solve business problems and produce relevant programming solutions.

Overview of local area network topologies, networking devices, and general architecture of local and wide area networks. Study of the Open System Interconnection model and application of standards and protocols. An introduction to communication principles and techniques.

Assessment: The module will be assessed through a case study project and a final exam. The case study project aims to assess students’ ability to analyse business requirements and design a network system for the given scenario concerning performance, maintainability and security guidelines, demonstrating their understanding of major networking concepts. The exam will comprehensively assess students' ability to grasp and apply the module material to given problems.

This module is designed to give students a foundational understanding of cybersecurity concepts and principles, covering core elements such as information security fundamentals, current threat landscape, different types of cyberattacks, security policies and governance, risk management, basics of cryptography and network security, secure software development and security education. A substantial amount of work will be laboratory based involving the deployment of security tools and the analysis of compromised systems.

Assessment: The module will be assessed by means of practical project and a PC-Lab Test. The practical project is based on a lab-based investigation and a portfolio penetration test report derived from lab results. PC-Lab Test will assess students` aptitude to grasp the module materials and additional reading.

The module allows students to demonstrate the knowledge obtained throughout the course. Students are expected to combine previously acquired knowledge and techniques with new ideas gained from investigation and research and produce an extended piece of work related to modern Computer Science challenges.

Assessment: The module is assessed by a Project Initiation Document, Progress report + Presentation, and a Final Project.

This module focuses on software testing and analysis processes, principles, and techniques. It introduces quality and quality assurance concepts, software QA techniques, testing approaches and techniques (control flow and data flow testing, mutation testing, specification-based testing techniques (equivalence partitioning, boundary value analysis, cause-effect graphing etc.), software quality management (test cycles, design and code reviews, test plans, test tools, quality metrics).

Assessment: The module will have two graded assessments: The PC-Lab Test will be time-constrained and require students to apply knowledge of software Testing and quality practices, use of tools and application of skills acquired in the module to solve given problems of certain scenarios. The examination will evaluate students’ comprehension of the theoretical aspects of the module.

The module provides an introduction to Natural Language Processing (NLP) for undergraduate students with a basic background in programming. Students will explore various NLP tasks, including text pre-processing, language modelling, part-of-speech tagging, named entity recognition, sentiment analysis, and machine translation. The course emphasizes practical applications and hands-on experience with NLP libraries such as NLTK, spaCy, and transformers.

Assessment: There are two assessment components: a Group Project and an In-class Lab Test.

The module covers distributed systems and cloud infrastructure. This course introduces the techniques underlying the design and engineering of distributed systems and cloud computing systems. Topics include distributed system models, computer clusters, virtualization, data centres, cloud computing models, cloud-enabling technologies, cloud mechanisms, and cloud architectures. Students will also acquire hands-on experience in cloud programming. Students will understand distributed databases, scalability, and consistency. They will develop and apply practical skills in building resilient, scalable systems.

Assessment: The module will be assessed through two components. The first is a Practical Coursework, weighted at 50%, which requires students to apply theoretical knowledge to practical tasks and produce structured, professional-standard work. The second component is an in-class PC Lab Test, also worth 50%. This test evaluates students’ individual understanding of key concepts and their ability to apply technical skills under timed conditions.

The module introduces basics of Internet of Things (IoT) systems, their component parts, connections, and communication methods. Emphasis is placed on practical experience with electronic kits, which include a microcontroller unit, sensors, and actuators. Students are required to build an electric circuit, a network, and write mobile or/and web applications to be able to control and monitor the built IoT system.

Assessment: The module will be assessed by portfolio, which will include a report with articulation of the case, research on existing solutions and justification for proposed solution, as well as circuit and the network of IoT system and control and monitor web/mobile application, accompanying viva voce with IoT system work demonstration.

This module will provide an overview of modern techniques in Machine Learning and Data Mining that are particularly customised for Data Science applications. Students will be introduced to a range of toolkits, such as R and Python and they will explore the features and strengths of different machine learning and data mining methodologies using selected data sets related to specific public sector or businesses application domains.

Assessment: The module will be assessed by a combination of coursework and an exam. The coursework will be an independent practical task (50%), where the task will be to fully implement data mining/machine learning techniques, focused on problem analysis, data pre-processing, data post-processing. The exam (50%) will assess the student’s ability to explain and discuss the techniques in Data Mining and Machine Learning.

The Network Security and Cryptography module offers a comprehensive overview of securing network communications and data. The module first introduces the students to the principles of cryptography, such as Symmetric and Asymmetric Key Cryptography, AES and RSA algorithms, and cryptographic hash functions. Then, it shows how to apply those principles to network security applications. Legal, ethical, and standard-setting considerations are discussed to ensure students are equipped to navigate evolving security standards and regulations effectively.

Assessment: The module will be assessed by means of practical project and time-constrained exam.

The module will introduce the Systems Development Life Cycle (SDLC) and guide students through the stages of planning, analysis, design, implementation, and maintenance of IT services in contemporary business organisations.

Students will be introduced to requirements gathering and system analysis techniques. They will learn to effectively collect business requirements and document them using industry standard tools such as UML, ERD and other diagrams.

The module introduces a variety of tools used by software analysts and designers for project management, system analysis, requirement documentation and implementation. Students will learn about different software development methodologies (Waterfall, Agile, RAD, XP, etc.). In addition, students will learn how to use UML 2.0 diagrams to document their system analysis and designs.

Assessment: Student performance in this module will be assessed through two pieces of coursework. Group Coursework makes up 40% of the final grade, while Individual Coursework accounts for the remaining 60%. Together, they assess both teamwork and independent problem-solving skills.

The module aims to introduce students to consumer neuroscience and neuromarketing methods. How do humans make decisions as consumers? How do their senses and initial responses affect their behavior? To what extent are these processes unconscious and cannot be reflected in overt reports? The module provides students with an introduction to some of the most basic methods in the emerging fields of consumer neuroscience and neuromarketing. Students learn about the basic brain mechanisms in consumer choice, the neuromarketing methods ranging from behavioral tests to eye-tracking, and to brain scanners. The module guides students through a contemporary literature review, giving insights into the last decades of development within this field of research.

Assessment: The assessment for this module includes two coursework, allowing students to demonstrate their comprehensive understanding and application of neuromarketing and AI principles throughout the course. Through the two CWs, a holistic evaluation of the students' abilities to integrate theoretical knowledge with practical skills will be integrated.

Students undertake part-time work of their choice, but relevant to the subject area and approved by the Module Leader, which can be paid or unpaid, equivalent to half day a week or 60 hours over the course of the module. Using a learning contract and a diary, students will keep a reflective learning log about their experiences at work and relationships to their academic studies.

Assessment: The module will be assessed by 100% portfolio, which is designed to enable assessor to understand what skills were offered to an employer, what objectives were formulated by both student and employer, what tasks were set to the student, what was the curve of execution of these tasks, what is overall feedback the student received for his performance, evaluation of a student of his current learning and student’s reflection on their development throughout their internship period.