Jamie Howard - Computer Science with a Year in Industry
Computer Science is an exciting and rapidly evolving subject that affects every area of our lives. A deep understanding of computing puts you in a great position to influence the future as well as opening up excellent employment prospects and well-paid careers.
At Kent, we teach you the fundamentals of computer science as well as giving you the opportunity to specialise in areas of your choice in your final year. Our excellence in teaching is underpinned by our research; we are an Academic Centre of Excellence in Cyber Security Research as well as being experts in programming languages and artificial intelligence. We give you lots of support via peer mentoring, web forums as well as study skills services to help you fulfil your potential.
You’re taught by experts in programming languages, data science, artificial intelligence and cyber security and learn to code in several languages, starting with the Java programming language, which is widely used in industry.
Building on these programming skills, you learn the principles and techniques that underpin the algorithms and systems shaping our world today. These include artificial intelligence, computer security, network technology, software engineering, and human-computer interaction. Using your knowledge you develop software in small-scale exercises before completing a major software project.
You can gain experience in teaching with our Computing in the Classroom module.
Full CITP and partial CEng accreditation from BCS, The Chartered Institute for IT.
Make Kent your firm choice – The Kent Guarantee
We understand that applying for university can be stressful, especially when you are also studying for exams. Choose Kent as your firm choice on UCAS and we will guarantee you a place, even if you narrowly miss your offer (for example, by 1 A Level grade)*.
*exceptions apply. Please note that we are unable to offer The Kent Guarantee to those who have already been given a reduced or contextual offer.
The University will consider applications from students offering a wide range of qualifications. All applications are assessed on an individual basis but some of our typical requirements are listed below. Students offering qualifications not listed are welcome to contact our Admissions Team for further advice. Please also see our general entry requirements.
Mathematics grade 4/C
The University welcomes applications from Access to Higher Education Diploma candidates for consideration. A typical offer may require you to obtain a proportion of Level 3 credits in relevant subjects at merit grade or above.
Distinction, Distinction, Distinction - Distinction, Distinction, Merit
30 points overall or 15 points at HL including Mathematics 5 at HL or SL, or Mathematics Studies 6 at SL
Pass all components of the University of Kent International Foundation Programme with a 50% overall average including 50% in Programming and 50% in LZ013 Maths and Statistics (irrespective of whether GCSE Maths or equivalent has been obtained in pre-foundation studies).
The University will consider applicants holding T level qualifications in subjects closely aligned to the course.
Typical entry requirements for 2022 entry remain published on the UCAS course search website. These provide a rough guide to our likely entry requirements for Clearing applicants.
During Clearing (after 5 July), our entry requirements change in real time to reflect the supply and demand of remaining course vacancies and so may be higher or lower than those published on UCAS as typical entry grades. Our Clearing vacancy list will be updated regularly as courses move in and out of Clearing, so please check regularly to see if we have any places available. See our Clearing website for more details on how Clearing works at Kent.
If you are an international student, visit our International Student website for further information about entry requirements for your country, including details of the International Foundation Programmes. Please note that international fee-paying students who require a Student visa cannot undertake a part-time programme due to visa restrictions.
Please note that meeting the typical offer/minimum requirement does not guarantee that you will receive an offer.
Please see our English language entry requirements web page.
Please note that if you do not meet our English language requirements, we offer a number of 'pre-sessional' courses in English for Academic Purposes. You attend these courses before starting your degree programme.
Find out more about what it's like to study Computer Science from the people who know.
There’s lots of space there, everything is well-equipped and you can always find somewhere quiet when you need to concentrate.
Jamie Howard - Computer Science with a Year in Industry
The experience I’ve gained during my year in industry is priceless.
Sey Kuyinu - Computer Science with a Year in Industry BSc
Register for Priority Clearing at Kent to give yourself a head start this results day.
Duration: 3 years full-time
The course structure below gives a flavour of the modules and provides details of the content of this programme. This listing is based on the current curriculum and may change year to year in response to new curriculum developments and innovation.
All modules are compulsory.
This module provides an introduction to object-oriented software development. Software pervades many aspects of most professional fields and sciences, and an understanding of the development of software applications is useful as a basis for many disciplines. This module covers the development of simple software systems. Students will gain an understanding of the software development process, and learn to design and implement applications in a popular object-oriented programming language. Fundamentals of classes and objects are introduced and key features of class descriptions: constructors, methods and fields. Method implementation through assignment, selection control structures, iterative control structures and other statements is introduced. Collection objects are also covered and the availability of library classes as building blocks. Throughout the course, the quality of class design and the need for a professional approach to software development is emphasised and forms part of the assessment criteria.
Mathematical reasoning underpins many aspects of computer science and this module aims to provide the skills needed for other modules on the degree programme; we are not teaching mathematics for its own sake. Topics will include algebra, reasoning and proof, set theory, functions, statistics and computer arithmetic.
This module follows from COMP3220 and aims to provide students with more understanding of the theory behind the formal underpinnings of computing. It will build upon the abstract reasoning skills introduced in COMP3220. Matrices, vectors, differential calculus, probability and logic will be introduced.
This module provides an introduction to human-computer interaction. Fundamental aspects of human physiology and psychology are introduced and key features of interaction and common interaction styles delineated. A variety of analysis and design methods are introduced (e.g. GOMS. heuristic evaluation, user-centred and contextual design techniques). Throughout the course, the quality of design and the need for a professional, integrated and user-centred approach to interface development is emphasised. Rapid and low-fidelity prototyping feature as one aspect of this.
This module equips students with an understanding of how modern cloud-based applications work. Topics covered may include:
• A high-level view of cloud computing: the economies of scale, security issues, ethical concerns, the typical high-level architecture of a cloud-based application, types of available services (e.g., parallelization, data storage).
• Cloud infrastructure: command line interface; containers and virtual machines; parallelization (e.g., MapReduce, distributed graph processing); data storage (e.g., distributed file systems, distributed databases, distributed shared in-memory data structures).
• Cloud concepts: high-level races, transactions and sequential equivalence; classical distributed algorithms (e.g., election, global snapshot, consensus, distributed mutual exclusion); scheduling, fault-tolerance and reliability in the context of a particular parallelization technology (e.g., MapReduce).
• Operating system support: network services (e.g., TCP/IP, routing, reliable communication), virtualization services (e.g., virtual memory, containers)
This module aims to strengthen the foundational programming-in-the-small abilities of students via a strong, practical, problem solving focus. Specific topics will include introductory algorithms, algorithm correctness, algorithm runtime, as well as big-O notation. Essential data structures and algorithmic programming skills will be covered, such as arrays, lists and trees, searching and sorting, recursion, and divide and conquer.
This module builds on the foundation of object-oriented design and implementation found in CO320 to provide both a broader and a deeper understanding of and facility with object-oriented program design and implementation. Reinforcement of foundational material is through its use in both understanding and working with a range of fundamental data structures and algorithms. More advanced features of object-orientation, such as interface inheritance, abstract classes, nested classes, functional abstractions and exceptions are covered. These allow an application-level view of design and implementation to be explored. Throughout the course, the quality of application design and the need for a professional approach to software development is emphasised.
The curriculum covers topics in algorithms and data structures, such as hashing and graph algorithms. It addresses how to program such algorithms, as well as how to test them, reason about their correctness and analyse their efficiency. It includes a mathematical treatment of big-O notation.
This module covers the basic principles of machine learning and the kinds of problems that can be solved by such techniques. You learn about the philosophy of AI, how knowledge is represented and algorithms to search state spaces. The module also provides an introduction to both machine learning and biologically inspired computation.
This module provides an introduction to the theory and practice of database systems. It extends the study of information systems in Stage 1 by focusing on the design, implementation and use of database systems. Topics include database management systems architecture, data modelling and database design, query languages, recent developments and future prospects.
This module introduces students to the functional programming paradigm, using at least one modern functional programming language to put the core concepts into practice. The module will develop both the foundation and theory of this paradigm, as well as the practice and application of the paradigm to solve problems and build systems. The module will core topics, including:
• Functions as first-class language constructs and as a central organising principle;
• Higher-order functions and compositional programming;
• Basic semantics of functional languages;
• The role of types in programming;
• Algebraic data types and pattern matching;
• Recursion and recursive data types;
• Differences with imperative and object-oriented programming paradigms;
• Properties of programs, (e.g., purity, side-effect freedom, totality, and partiality).
• The lambda-calculus as a programming model and foundation.
• BNF grammars for representing context-free syntax, and its relation to ADTs and language manipulation.
• Testing and issues of building correct software.
The module will develop practical skills in programming and problem solving using functional programming. There will also be a chance to apply functional programming to help understand better concepts in logic and mathematics.
Later parts of the module will then consider concurrent programming in the context of functional programming, including concurrent programming models and primitives (e.g., message-passing concurrency), parallelism, synchronisation and communication, and properties of deadlock, communication-safety, and starvation.
This module aims to provide students with an understanding of the fundamental components (hardware and software) of a typical computer system, and how they collaborate to execute software programs. The module provides a compressive overview from the lowest level of abstractions in hardware to the highest level of abstractions of modern programming languages. For example, they will see logic circuits, machine language, programming language implementations, high-level languages, and applications. This material provides a general understanding of computers, and it will also prepare students to develop software considering the system perspective, e.g. cost of abstraction and performance implications.
Cyber security has always been an important aspect of computing systems but its importance has increased greatly in recent years. The curriculum covers areas where cyber security is of major importance, but have different security requirements and may be exposed to different threats and attacks. It also covers techniques and mechanisms used to secure computer systems and data to meet those requirements and protect them. The areas looked at include computer operating systems (and increasingly, distributed operating systems), distributed applications (such as electronic commerce over the Internet), embedded systems (ranging from smart cards to large industrial plant and telecommunications systems), and users. The techniques and mechanisms looked at include cryptography, authentication & authorisation, and access control. Furthermore, the curriculum integrates the legal, ethical, and professional perspectives, for instance, to address concerns about data security, privacy, and societal impact of computing systems.
The module studies team-based Agile software development in detail and places it in a wider software development context.
Topics covered include
• Concepts, principles, practice and philosophy of an Agile approach to software development, contrasting with more structured approaches.
• Collaboration: programmer collaboration, team values, customer involvement, project management, standards and reporting.
• Planning: release and sprint planning, risk assessment, user stories and resource estimating
• Development practices: incremental requirements, test-driven development, refactoring, scrum, code review, quality assurance, continuous integration.
• Tools: IDEs, version control, automated code quality evaluation, issue tracking.
• Ethics, Intellectual property, codes of conduct and professional responsibility.
Propositional & Predicate Logic, including proofs. Formal languages: finite automata, regular expressions, CFGs. Turing machines, decidability.
Building scaleable web sites using client-side and and server-side frameworks (e.g. JQuery, CodeIgniter). Data transfer technologies, e.g. XML and JSON. Building highly interactive web sites using e.g. AJAX. Web services. Deploying applications and services to the web: servers, infrastructure services, and traffic and performance analysis. Web and application development for mobile devices.
This module is designed to provide students across the university with access to knowledge, skill development and training in the field of entrepreneurship with a special emphasis on developing a business plan in order to exploit identified opportunities. Hence, the module will be of value for students who aspire to establishing their own business and/or introducing innovation through new product, service, process, project or business development in an established organisation. The module complements students' final year projects in Computing, Law, Biosciences, Electronics, Multimedia, and Drama etc.
The module starts with a comprehensive and detailed study of current computer networks and communications technologies. It includes: a review of network techniques, switching and multiple access; high speed local area networks; network protocols, including data link, network, transport and application layers. A selection of key topics are looked at in greater depth to reveal the state-of-the-art and issues (problems) that remain to be solved.
In this module you learn what is meant by neural networks and how to explain the mathematical equations that underlie them. You also build neural networks using state of the art simulation technology and apply these networks to the solution of problems. In addition, the module discusses examples of computation applied to neurobiology and cognitive psychology.
There is an increasing use of nature-inspired computational techniques in computer science. These include the use of biology as a source of inspiration for solving computational problems, such as developments in evolutionary algorithms and swarm intelligence. It is therefore proposed to allow students the opportunity to become exposed to these types of methods for use in their late careers.
"Indicative topics include:
• Resource Description Framework (RDF) & RDF Schema:
• Information representation and knowledge exchange on the web
o Applications of RDF
• RDF Query and Inference Languages (e.g. SPARQL etc.)
• Web Ontology Language (OWL):
o Publishing and sharing of ontologies
• Knowledge management
Students will spend one half-day per week for ten weeks in a school with a nominated teacher. They will observe sessions taught by their designated teacher and possibly other teachers. Later they will act somewhat in the role of a teaching assistant, by helping individual pupils who are having difficulties or by working with small groups. They may take 'hotspots': brief sessions with the whole class where they explain a technical topic or talk about aspects of university life. They must keep a weekly log of their activities. Each student must also devise a special project in consultation with the teacher and with the module convener. They must then implement and evaluate the project.
The following is indicative of topics/themes this module will include:
• An overview of basic concepts related to Computational Intelligence (CI) techniques, such as heuristic search and optimisation
• Presentation of different CI algorithms, such as hill climbing, simulated annealing, genetic algorithms and genetic programming
• An overview of basic concepts related to real-world problems related to business, economics and finance, such as financial forecasting, automated bargaining, portfolio
optimisation, and timetabling
• The use of Computational Intelligence techniques to solve real-world problems
• Computational Intelligence decision support systems and software wind tunnels for testing new markets and strategies.
The module will cover a mixture of theoretical and practical topics in the area of the Internet of Things (IoT), that is, the use of Internet technologies to access and interact with objects in the physical world. This will include coverage of the range of sensor and actuator devices available, ways in which they communicate and compute, methods for getting information to and from IoT-enabled devices, and ways of visualising and processing data gained from the IoT. A practical component will consist of building the hardware and software for a sensor network and a system to collect, process and visualise data from that network.
A study of techniques for interpreting and compiling programming languages, implementing them in a typed functional programming language (e.g., OCaml, Haskell). The module will outline a whole compiler from source to machine code, but will focus in depth on key algorithms and techniques. Possible in-depth topics include:
• writing interpreters,
• Hindley-Milner type inference,
• register allocation,
• garbage collection,
• abstract interpretation,
• static single assignment form.
The implemented language will be based on a simple imperative (e.g., Pascal-like) language with some extensions to address advanced topics in data layout (e.g., closures, objects, pattern matching). The course will be organized around a simple, but complete, example compiler that the student will have to understand and modify.
The module aim is to give students an overview and understanding of key theoretical, practical and philosophical research and issues around computational creativity, and to give them practical experience in writing and evaluating creative software.
The following is an indicative list of topics that may be covered:
• Introduction to computational creativity
• Examples of computational creativity software e.g. musical systems, artistic systems, linguistic systems, proof generator systems, systems for 2D and 3D design.
• Evaluation of computational creativity systems (both of the quality and the creativity of systems)
• Philosophical issues concerning creativity in computers
• Comparison of computer creativity to human creativity
• Collaborative creativity between humans and computers
• Overview of recent research directions/results in computational creativity
• Practical experience in writing creative software.
This module is aimed at introducing the principles of concurrency theory (1, 2, 3) and demonstrating how these can be applied to design and implement distributed applications (4). Advanced concepts of Web services will be studied and placed in the perspective of these principles (5, 6).
The following is an indicative list of topics:
• Message passing primitives for concurrency: synchronous versus asynchronous message passing, the actor model.
• Reasoning on processes: temporal logic, safety and liveness properties, bisimulation.
• Channel passing and mobility.
• Design and implementation of application–level protocols.
• Web services: from stateless services to distributed business processes (also known as service orchestrations).
• Transaction protocols on the Web: two-phase commit, long running transactions.
This module will provide the student with an understanding of basic principles of signals; introduce digitisation methods such as sampling, quantisation and coding; describe and apply signal analysis techniques, such as segmentation, noise reduction, filtering, spectral analysis, feature extraction and classification (including recognition and decision making) to solve practical signal analysis problems using Matlab.
An overview of basic concepts related to eHealth and a perspective on current HIT (Health Information Technology) and innovation. Review of current healthcare related IT systems. The use of information technology for handling clinical data, health systems. Data representation and knowledge management. Security and privacy. Ethics and legal requirements of eHealth systems. Clinical decision support systems. TeleHealth tools for remote diagnosis, monitoring, and disease management. Delivery and monitoring platforms for both hospitals and home environment. Innovation in eHealth systems leading to start-up companies.
This module will give students an overarching introduction to quantum information processing (QIP). At the end of the course the students will have a basic understanding of quantum computation, quantum communication, and quantum cryptography; as well as the implications to other fields such as computation, physics, and cybersecurity.
We will take a multi-disciplinary approach that will encourage and require students to engage in topics outside of their core discipline. The module will cover the most essential mathematical background required to understand QIP. This includes: linear algebra, basic elements of quantum theory (quantum states, evolution of closed quantum systems, Born's rule), and basic theory of computing. The module will introduce students to the following theoretical topics: quantum algorithms, quantum cryptography, quantum communication & information. The module will also address experimental quantum computation & cryptography.
This module explores a range of different data mining and knowledge discovery techniques and algorithms. You learn about the strengths and weaknesses of different techniques and how to choose the most appropriate for any particular task. You use a data mining tool, and learn to evaluate the quality of discovered knowledge.
This module is concerned with a range of topics in video game design and development, including game physics, AI, level design, player behaviour, game rules and mechanics, as well as user interfaces. This module introduces students to game development using industry-standard software tools.
The module introduces fundamental techniques employed in image processing and pattern recognition providing an understanding of how practical pattern recognition systems may be developed able to address the inherent difficulties present in real world situations. The material is augmented with a study of biometric and security applications looking at the specific techniques employed to recognise biometric samples.
This module introduces the theory and practice of employing computers as the control and organisational centre of an electronic or mechanical system, and examines issues related to time critical systems. It also provides exposure to practical embedded systems design through practical work, with one assignment exploring the ideas of real-time operating systems introduced in the lectures and a second using a microcomputer programmed in 'C' to control the ignition timing of a simulated petrol engine.
The module will study some of the major works in the history of modern philosophy of cognitive science and artificial intelligence. An indicative list of topics is: The Turing test; the Chinese Room argument; the frame problem; connectionism; extended and embodied cognition; artificial consciousness. The approach will be philosophical and critical, and will involve the close reading of texts. Students will be expected to engage critically with the works being studied and to formulate and argue for their own views on the issues covered.
The 2022/23 annual tuition fees for this course are:
For details of when and how to pay fees and charges, please see our Student Finance Guide.
For students continuing on this programme, fees will increase year on year by no more than RPI + 3% in each academic year of study except where regulated.*
The University will assess your fee status as part of the application process. If you are uncertain about your fee status you may wish to seek advice from UKCISA before applying.
Within the School of Computing are authors of widely used textbooks, a National Teaching Fellow and Association of Computer Machinery (ACM) Award-winning scientists. Programmes are taught by leading researchers who are experts in their fields.
Teaching is based on lectures, with practical classes and seminars, but we are also introducing more innovative ways of teaching, such as virtual learning environments and work-based tuition. Work includes group projects, case studies and computer simulations, with a large-scale project of your own choice in the final year.
Each stage comprises eight modules. Most modules run for a single 12-week term and have two lectures and one to two hours of classes, making 14 formal contact hours per week and eight hours of 'homework club' drop-in sessions each term.
We provide excellent support for you throughout your time at Kent. This includes access to web-based information systems, podcasts and web forums for students who can benefit from extra help. We use innovative teaching methodologies, including BlueJ and LEGO© Mindstorms for teaching Java programming.
Our staff have written internationally acclaimed textbooks for learning programming, which have been translated into eight languages and are used worldwide. A member of staff has received the SIGCSE Award for Outstanding Contribution to Computer Science Education. The award is made by ACM, the world's largest educational and scientific computing society.
Assessment is by a combination of coursework and end-of-year examination and details are shown in the module outlines on the web. Project modules are assessed wholly by coursework.
The marks from stage one do not go towards your final degree grade, but you must pass to continue to stage two.
Most stage two modules are assessed by coursework and end-of-year examination. Marks from stage two count towards your degree result.
Most stage three modules are assessed by a combination of coursework and end-of-year examination. Projects are assessed by your contribution to the final project, the final report, and oral presentation and viva examination. Marks from stage three count towards your degree result.
In stage three your project counts for 25% of the year's marks.
For a student studying full time, each academic year of the programme will comprise 1200 learning hours which include both direct contact hours and private study hours. The precise breakdown of hours will be subject dependent and will vary according to modules. Please refer to the individual module details under Course Structure.
Methods of assessment will vary according to subject specialism and individual modules. Please refer to the individual module details under Course Structure.
The programme aims to:
You gain knowledge and understanding of:
You develop intellectual skills in:
You gain subject-specific skills in:
You gain transferable skills in:
Our graduates have gone on to work in:
Recent graduates have gone on to develop successful careers at leading companies such as:
The University has a friendly Careers and Employability Service, which can give you advice on how to:
You have access to a dedicated Employability Coordinator who is a useful contact for all student employability queries.
You graduate with a solid grounding in the fundamentals of computer science and a range of professional skills, including:
To help you appeal to employers, you also learn key transferable skills that are essential for all graduates. These include the ability to:
You can also gain extra skills by signing up for one of our Kent Extra activities, such as learning a language or volunteering.
Our Computer Science degree has full Chartered IT Professional (CITP) accreditation from the BCS, The Chartered Institute for IT.
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