In this module, students will develop an understanding of the ethical, professional and legal issues relating to computer science. Students will develop critical investigative skills in applying ethical theories to technological outcomes vis-à-vis software engineering, cyber security and digital forensics. 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. They will develop their understanding of a range of computer science sources and evaluate their reliability and validity. Students will participate in critical reading, writing and develop their referencing techniques.
This module covers the fundamental discrete mathematics required by a computer science student in order to model and analyse the problems that arise in computer science. It lays the foundations for students to successfully complete the remainder of their studies as well as for a computing career. Vectors and matrices are the mathematical models underlying computer graphics, machine learning and deep learning. Logic is a tool used to reason about computer programs as well as the real world. Recursion is an important programming principle that comes with an associated proof rule, and other mathematical notations such as functions and relations are used routinely in computer science. Theoretical computer science can be considered an area of mathematics, and this module provides an introduction to the fundamental notions of this area.
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 traduces problem-solving and structured procedural and object-orientated programming. In the first half of the module, students are introduced to the components and constructs of procedural programming. Emphasis is placed on designing, developing and deploying correct, efficient, maintainable and scalable programs. The second half of the module provides students with a thorough introduction to computer data structures and algorithms in the context of object-orientated programming. The construction of well-designed interfaces, program encapsulation and abstraction are discussed 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 programs 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 will introduce students to concepts involved in the fundamentals of computer architecture, operating systems and networking. The emphasis is on the hardware aspect of a system, and how hardware is used during the execution of software. In this module, students develop the knowledge, skills and understanding required to comprehend computer systems, be they terminology, models, methodologies, structures, number representation and a general introduction to 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. Network fundamentals cover network architecture, structure and functions. It introduces the principles and structures of Internet Protocol (IP) addressing, fundamentals of Ethernet concepts, media and operations. This module aims to serve as an essential foundation for further studies in the single honours Computer Science degree programme.
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 concept data modelling, 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.
- G400 Course Code
- 3 Years
- 96 Typical UCAS Tariff
Summer Open Days
Join us for our Summer Open Days on Wednesday 8th June (4pm-8pm) or Saturday 9th July (10am-3pm). Representatives from each of our subject areas and student support departments will be available to speak to. There will be a number of subject talks, and the opportunity to tour the Newman campus.Book Now
You should aim to achieve 96 UCAS points including a minimum of CC at A level or equivalent (e.g.MM at BTEC Diploma; MPP at BTEC Extended Diploma) towards the total tariff.
Access Students can can achieve the requirements with a combination of Distinction, Merit and Pass grades at level 3 achieved from a completed Access course.
96 UCAS Points:
D21-M3-P21; D18-M9-P18; D15-M15-P15;
D12-M21-P12; D9-M27-P9; D6-M33-P6;
Five GCSEs at grade 4 (or C) or above (or recognised equivalents), including Maths and English Language, are also required.
The University is not licensed by the UK Government to sponsor migrant students under the Student route and is therefore unable to accept applications from international students at present.
Applying Direct Option
You can apply direct to Newman University for this course if you have not previously applied to Newman University through UCAS and you are not applying to any other universities.
Simply click on this Direct Application link to do this.
N.B. will need to enter ‘New User’ account details when first accessing this portal.
If you have any questions regarding entry onto this course please contact our friendly and helpful admissions team via our Admissions Enquiry Form
The full-time course fee for September 2022 is £9,250.
The University will review tuition fees and increase fees in line with any inflationary uplift as determined by the UK Government, if permitted by law or government policy, in subsequent years of your course. It is anticipated that such increases would be linked to RPI (the Retail Price Index excluding mortgage interest payments).
Find out more about the other additional costs associated with our undergraduate degrees.
This module provides the opportunity to mesh theoretical and practical learning activities. Students will be required as a key part of the module and its assessments to undertake a work placement in a computer science or computer science related setting for one hundred hours. Students will participate in a work-based learning experience, such as data collection, data analytics, mobile app development, or cybersecurity. The activity must contain elements of research, including a systematic review of the literature, critical analysis, ethical considerations and dissemination. The module also contains an element of reflection of digital skills for the workplace, reflecting on professional development, working with others and work-based learning.
The popularity of the Internet, coupled with the explosive growth of mobile computing devices (such as smartphones, personal digital assistants (PDAs) and laptops), has led to the world of universal electronic connectivity. In this connected world, people access data and services on the Internet and communicate with each other anywhere and anytime. While this level of connectivity may bring us many benefits such as improving our quality of life and running services with global presence but with reduced costs, it does open vast opportunities for unauthorised access to data, services and other resources and for fraud and forgeries in commercial and business activities. Therefore, it is of paramount importance to increase both awareness and knowledge in protecting data and resources from unauthorised disclosure, in guaranteeing the authenticity of data in transit, and in protecting networked systems against attacks. The first part of the module covers the fundamental concepts required to understand and to work with security. These security concepts include: historical background, security and security threats, practical measures (such as identification, authentication, audit trails), 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 security will be discussed and possible solutions will be investigated.
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.
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: supervised learning, unsupervised learning and best practices in machine learning. Case studies and applications will be used to illustrate best practice in machine learning.
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.
In this module, students will address the general problem of sensor-based mobile robot navigation and explore the implementation of intelligent behaviour in mobile robots. Throughout the module, students will be introduced to different characteristic problems in robotics, derive mathematical models for those problems, develop algorithms that apply these mathematical models to solve robotic problems, translate proposed algorithms into a program, and implement and test these programs on a mobile robot.
The module presents the student with the opportunity to articulate creative ideas through computing. At the end of the module, students will understand the foundational creative processes in the form of computer programs that produce audio-visual content to a high standard. The module provides the foundational aspects of programming for creativity, principles of form, structure, transformation and generative processes for image, sound and video. Students will explore the methods and conceptual tools used in the creative industries. They will also implement creative concepts that are not easily realised with commercial software packages. A level of originality would be expected in their creative work. Students will also understand the main concepts of signal processing, investigate how perception works, and apply their knowledge to produce innovative creations.
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 forms an alternative option for undertaking the 40-credit capstone final project. It is designed to offer students ‘real life’ problem-solving skills in undertaking a work-based related project. Learners should complete a minimum of 100 hours in the workplace. The project is often employer-generated or can be negotiated with an employer in a computer science or computer related field. The completed project should be of benefit to the organisation.
Deep Learning has shown its success in many areas including computer vision, speech and audio processing, natural language processing, robotics, bioinformatics and chemistry, video games, search engines, online advertising and finance. Deep Learning is a particular kind of Machine Learning technique that allows computer systems to improve with experience and data, and achieves great power and flexibility by learning to represent the world as a nested hierarchy of concepts. In recent years, deep learning has seen tremendous growth in its popularity and usefulness, due in large part to more powerful computers, larger datasets and techniques to train deeper neural networks. This module introduces a wide range of deep learning and other state of art techniques in Artificial Intelligence for solving real-world problems. Basic concepts on statistics and applied maths that thread through key elements in Machine Learning techniques will be discussed throughout the module. Students will study how to build suitable Artificial Intelligence systems that can operate in complicated, real-world environments. The module also prepares students to explore further challenges and opportunities to improve deep learning and Artificial Intelligence and bring them to new frontiers.
This module aims to teach students the concepts, skills and knowledge required to develop, design and prototype games. This module provides an introduction to games design and the games industry and outlines the key elements of game development. The module will provide contextual knowledge of the games industry (past and present; the UK and international), different genres, technology and tools, and roles, good practice, and current challenges of games design and development. The wider influence of games within contemporary culture and education will also be considered, specifically the application of ludic design principles in other creative sectors (e.g. ‘gamification’).
In this module, students have the opportunity to learn and reflect upon how computer science is taught in schools both as a passive observer and an active participant. Students will act as ambassadors for the subject by sharing their knowledge of and passion for computer science, motivate learners and hopefully inspire them to understand an aspect of the beauty and joy of computer science. Working alongside an in-service computing teacher, students will have the opportunity to communicate computer science concept(s) to learner(s) and reflect upon this experience. This experience will enable students to develop transferable employability skills; such as organising and planning, communication, and working within a professional team; within a professional environment. For those students who wish to pursue a career as a computing teacher teaching computer science, then this module provides a vehicle for gaining valuable insight into not only how computer science is taught in schools but also the role and responsibilities of a computing teacher. In order to take this module, students will be required to attend orientation training regarding working with learners and conducting themselves within a school-based setting. This orientation will be held in the first semester of the final year of this programme. This module has designed in accordance with the recommendations and guidance of the national Undergraduate Ambassador Scheme (UAS).
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 be provided with a wifi 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.
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.