The course is ideal for students who are interested in developing and applying problem-solving skills to real world problems, would like to develop their understanding of computing, mathematics and statistical techniques through the practical lens of artificial intelligence (AI). With a balance of solid theory and practical application, this course builds on knowledge in relevant areas of statistics, data analysis, probability and programming.
The over-arching aim of the Computer Science and Artificial Intelligence course is to produce highly trained graduates with specialist technical knowledge in the mathematical and computational science aspects of applied AI, capable of solving real world problems with understanding of the wider socio-technical implications.
Attendance | UCAS code/apply | Year of entry |
---|---|---|
3 years full time | I100 | 2023 |
4 years full time including sandwich year | I101 | 2023 |
4 years full time including foundation year | I102 | 2023 |
6 years part time | Apply direct to the University | 2023 |
Our programme structure is designed to encourage students to become more effective, independent and confident self-directed learners which appeals to employers and aligns with our Future Skills strategy. Supported by a set of guided learning journeys, you will learn theoretical and practical aspects applied computer science and AI, and gradually develop a portfolio of 'products' and 'artefacts' of different levels of complexity as the outputs of assignments in each module.
Option modules are selected in the spring term, guided by course leader and personal tutors. They are designed to allow students to specialise or tailor the course to meet their individual career goals towards roles in the broad area of "data science" e.g. by including user experience modules, or towards more specialist computer science areas by taking option modules in programming or software development.
Please note that this is an indicative list of modules and is not intended as a definitive list. Those listed here may also be a mixture of core and option modules.
This first year provides a broad exposure to the essential domain topics: computing fundamentals, programming, professional practice and mathematics for artificial intelligence.
30 credits
This is a core Level 4 module for the BSc in Computer Science and Artificial Intelligence. Students will be introduced to the key mathematical functions and concepts that are at the foundation of AI within the broader field of computer science. You will become familiar with fundamental concepts and notation, and will learn about topics including logic, set theory, number systems, linear algebra, elementary calculus and introductory probability, and how they are used in AI.
30 credits
This module is taken by all first year undergraduate students undertaking a degree in the computing subject area. Previous experience of programming is not assumed. The module seeks to introduce a foundation for programming that can be built on in subsequent years and that accommodates specialist practice within computing, e.g. games, software engineering, media, UX etc.
Teaching and learning is split between a variety of different units to ensure the module is flexible enough to accommodate each cohort and student's needs. As befits a practical discipline like programming, a hands-on approach is used that facilitates self-paced and self-directed learning. Students are encouraged to engage with, develop and experiment with programs in a constructivist fashion inspired by bricolage (Stiller, 2009; Stiller, 2017).
The intent is to build students' confidence as they learn to program, and provide a foundation that can be built on so that in later years they can go beyond simple solutions to problems and be ready to engage in fully-fledged application development.
30 credits
This module introduces students to the principles behind hardware and software systems, and the important concepts related to modern computer systems.
Firstly, following a review of the relevant mathematical principles, students will acquire an understanding of computer architecture, how data are represented, stored and processed, and how the operating system manages hardware and software resources.
Secondly, students will understand the main concepts behind databases, network communication, and social media. Finally, they will learn about the essential technologies supporting web development and database management.
30 credits
The goal of the Professional Environments module is to prepare students for professional practice. It will firstly ensure they acquire suitable employability assets and secondly equip them with an understanding of the role of a professional in society and the role of professional bodies.
While the bulk of the taught programme focuses primarily on domain knowledge, the Professional Environments module focuses on developing key skills, personal qualities (e.g. commercial awareness, reliability and punctuality, understanding the centrality of customers and clients), and professional knowledge including the need to engage with continuing professional development. With such assets, students will generate a CV, an employment portfolio, and a professional online presence.
Being a professional also means understanding the key legal, ethical and societal issues pertinent to the domain, and understanding the need for continuing professional development (CPD) especially when technology develops at such a rapid pace. The module is designed to support different domain areas and to integrate experience from other professions. The subject areas being studied demand a global perspective which encourages the inclusion of our diverse of communities and national practices.
Reflecting the fact that team working is ubiquitous in the modern workplace, a significant proportion of the assessment work on the course is based around group work. There is considerable evidence that group work promotes a much deeper engagement with taught content and the Future Skills report shows how it is embedded in working practices. It also encourages the development of diverse learning communities with computer science, cybersecurity and digital media students working in close proximity. This module will therefore introduce students to best practice in group working covering how to approach group work, how to understand yourself, how to deal with different types of people, and methods of selecting and managing groups.
30 credits
This module is a Level 5 core module for the BSc in Computer Science and Artificial Intelligence. It builds upon the skills students will have acquired in the Level 4 modules, primarily in mathematics, statistics and programming, and will provide them with a solid foundation in A.I. in preparation both for their Individual Project and other modules at Level 6, or for an appropriate Industrial Placement (if taken).
This module will introduce students to the principal paradigms of Artificial Intelligence, and their relation to other disciplines, such as Cognitive Science, and their applications to practical problems. These will include both traditional (logic and search based) and more modern (Machine Learning) approaches (including neural networks), and will also cover some real-world practical case studies.
It will cover the "core" mathematical, statistical and machine learning concepts that are essential attributes for employable data scientists and machine learning engineers in the modern, data-driven world, and include an introduction to the importance of ethical debate in both the development and application of A.I., with contemporary case studies, and issues relating to data privacy and data protection.
15 credits
This module seeks to establish the skills required to effectively use databases for storage and retrieval of information. Students will learn how to design, build and query databases according using logical data models and structured query language (SQL).
The module aims to develop theoretical and practical knowledge to design, develop and manage data models using modern database management systems.
On successful completion of the module, students will be able to:
15 credits
This module builds upon the skills acquired in Level 4 and will deepen students' knowledge and understanding of mathematical concepts that underpin aspects of Computer Science and Artificial Intelligence.
The module can be considered to have two components.
The first develops the mathematical skills required to understand the principles of probabilistic and statistical methods in the context of data analysis. This is followed by an introduction to descriptive statistics and probability distributions, through to applications of applied statistical techniques which are fundamental to the exploration, data insight, and modelling techniques, which are in turn, essential in making sense of datasets, regardless of size.
The second component covers some of the more practical aspects of data analysis and visualisation in practice and will be taught with a highly hands-on, data driven, application led approach.
Students will be introduced to the practicalities of using real life data covering processes for collecting, manipulating and cleaning data and of quality assurance of data sources. This module will provide students with basic data modelling and interpretation skills that feeds into data-driven decision-making. The module focuses on applicable and practical skills commercially and contextually relevant software and techniques.
30 credits
Following a project-based pedagogic approach, students will undertake a major inter-disciplinary team-work project drawn from a list of authentic industrial problems. Achieving the goals of the project will require students, firstly, to apply the various development methodologies they have acquired on their course and, secondly, to develop professional skills in project management and team working.
While the bulk of the taught programme focuses primarily on the learning of domain knowledge, the goal of the Professional Environments 2 module is to prepare students for professional practice in their respective domains. They will develop the necessary project management and team-working skills. By working as a team on an authentic industrial project, they will gain a high degree of familiarity with the capture, design, and development methodologies relevant to their discipline. With the focus on making real-world artefacts, the students will integrate their work into an employment-focused portfolio.
Being a professional practitioner also means critically assessing goals and solutions from legal, ethical and societal perspectives as well as addressing security and safety concerns. Students are encouraged to consider their continuing professional development needs and to engage with their professional bodies. To encourage career management skills and promote employability after graduation, students are expected to integrate the artefacts they produce and reflective practice narratives into their employability portfolios and personal development plans.
The module is designed to support different domain areas and to integrate experience from other professions. The subject areas being studied demand a global perspective which encourages the inclusion of our diverse of communities and national practices.
30 credits
This module seeks to extend your understanding and proficiencies in the fundamental concepts of programming, giving you the ability to build complex applications across a variety of platforms and channels. You will be exposed to different programming paradigms including a comprehensive treatment of the object-oriented paradigm, selection and use of data structures, use of libraries and APIs including user interface components. It will also introduce important tools and techniques used by software development teams in such as integrated development environments, revision control systems, dependency management, code profiling and optimisation techniques. Although the module focuses on the implementation stage, it links the implementation with different software development methodologies. It also provides links with the other stages of the software development life cycle.
30 credits
The module will enhance your understanding of how modern computer systems are implemented from the perspectives of architecture, networking, operating system, parallel programming and algorithm complexity. You will explore the essential features and operations of modern computer architectures and acquire both theoretical and practical knowledge of the principles and major functions of modern operating systems. You will also develop knowledge of parallel programming and algorithm complexity so that you will be able to make use of new parallel computer architectures. Physical networks and their associated address schemes will also be explored.
30 credits
This module will equip students with knowledge and experience in the concepts and methods in machine learning and artificial intelligence needed to evaluate their applicability to real-world problems. Students will apply machine learning and artificial intelligence algorithms and techniques in appropriately-scaled real-world applications. They will also gain a thorough awareness and understanding of the importance of ethical issues in A.I. and its applications, and can carry out an assessment of the ethical implications of particular developments and applications of A.I.
On successful completion of the module, students will be able to:
30 credits
This module will consolidate and build on previously acquired knowledge of databases by analysing and evaluating important issues in the database area. In addition, advanced aspects of data warehousing and data mining will be studied, encompassing the principles and commercial application of the technologies.
30 credits
The goal of the module is to further develop skills in organisation, timekeeping, research literature, developing and critically analysing results as well as reporting work verbally and in a written format. The end result will be an artefact or artefacts which demonstrate creativity and technical competence as well as a technical report.
On successful completion of the module, students will be able to:
30 credits
This module aims to provide a strong theoretical and practical background necessary for you to build high quality scalable software and to operate effectively as an industry professional. It examines software quality concepts necessary to build high quality software architecture. The module introduces you to the concept of software architecture and architectural patterns as part of software design and reuse which can be viewed as components and interfaces. At a lower level, programming models and paradigms are explored, as well as design patterns and anti-patterns. Testing strategies and other software quality principles will also be covered, and you will explore these principles in the context of practical projects which expose you to industry tools, practices and management methodologies.
30 credits
This is an optional module intended for undergraduate students who are studying Computing-related subjects. Although it forms part of the User Experience guided pathway it can be taken as a standalone module and previous experience of UX is not assumed. This module will focus upon the skills, methods and tools required in careers such as UX Architect, UX Designer, Service Designer, Information Architect or Digital Product Designer. The curriculum is finely balanced between theory and practice. Students are directly immersed in organisational practices and skills used in industry and will make use of academic theory in this practical context. Students will learn to develop investigative, analytical, technical, communication and advocacy skills to help them shape interactive technologies that augment people's abilities, enhance their creativity, connect them to others and protect their interests. They will also become aware of the impact of levels of digital literacy, availability of and access to technology, economic and business drivers, regulations, and regional/cultural norms. The module will also develop methods and skills required to understand current users, to investigate non-use, and to imagine future users.
30 credits
While this module provides a foundation for careers in mobile application development, mobile is becoming increasingly ubiquitous and the skills taught also have applications in UX, web development and software engineering in general. Although there are no prerequisites, it is assumed that students have acquired a general familiarity with programming and software development principles through their previous study.
The module is divided into two phases. In the first phase, students will be introduced to software development for the two major mobile platforms. This will cover development environments for these platforms, UI conventions, building and deploying simple applications. Students will then be introduced to cross platform development environments for mobile development.
In the second phase, standard frameworks for mobile web development will be introduced. The phase is organised around a practical project. Students will choose one of the platforms on which to build a mobile application of their choice. This project gives students the opportunity to specialise and explore their chosen platform in greater depth, acquiring the knowledge and proficiency to be able to design and build complex mobile apps. Students will be encouraged to publish their apps in one or both (in the case of a cross-platform app) of the two major app stores, thus providing an introduction to mobile application delivery and distribution.
30 credits
Programming is a central activity of software development, which encompasses a wide range of languages, environments and specialisms.
This module will offer students the opportunity to acquire a useful competence across this range. The first teaching block will cover aspects of language, algorithms, tools, test-driven methodologies, and a range of user interface technologies.
The second teaching block will apply these themes across a range of technologies and application environments, focusing on web and mobile in particular. Assessment will include computer-based in-class tests and e-portfolios of student achievement that allow them to curate and share their passion for programming.
15 credits
This Level 6 option module is oriented towards Data Science, a significant and essential subfield of modern Computer Science and Artificial Intelligence. Data science has many application areas ranging from medicine to climate science and business analytics.
This module builds on several topics covered in earlier parts of the programme including mathematical and statistical modelling, probability estimation and visualisation. Students will be introduced to the key statistical tests for frequentist data science, where specific theories are explored and tested via descriptive and inferential statistics. They will apply their existing programming knowledge to execute these tests in an appropriate programming language using existing statistical libraries.
Students will learn to write computer programs that can read, process and analyse textual and numerical data, for the purposes of data insight and interpretation. This module will provide students with a data science skillset to include critical thinking, communication, teamwork and leadership skills that feed into data-driven decision-making. The module focuses on practical applications to hone commercially and contextually relevant knowledge and competences.
15 credits
The module initially begins by using and building upon the distribution theory met in levels 4 and 5. Students are introduced to the important aspects of statistical estimation including the Neyman-Pearson's lemma with its useful and interesting applications. We then embark on the Bayesian methodology and its applications to the statistical decision-making problems including loss and risk functions. In particular, we make use of both Bayesian actuarial credibility theory and the evaluation of the credibility coefficient. Furthermore, practical applications of Bayesian methodology are illustrated and compared with classical approach. and the advantages involved in decision making are identified and discussed.
15 credits
This module comprises the application of time series modelling techniques in forecasting with background gained in earlier modules. We cover both non-probabilistic algorithmic methods and the probabilistic Box-Jenkins ARIMA modelling techniques. The methods are applied to real and up-to-date time series data sets using MS Excel and SAS Enterprise software packages. Emphasis is placed on practicability of methods and illustration of their real-world applications. Students will have the opportunity to acquire, develop and consolidate both modelling and software skills through a series of exercises during practicals and tutorial sessions.
Optional modules only run if there is enough demand. If we have an insufficient number of students interested in an optional module, that module will not be offered for this course.
Foundation year
If you would like to study computing at Kingston University but are not yet ready to join the first year of a BSc (Hons) course, you can include an extra foundation year within your chosen degree. Please see the foundation year course page for details of modules.
There is a wide range of facilities at our Penrhyn Road campus, where this course is based. You will have access to a modern environment with the latest equipment, including:
Our dedicated team of IT technicians support the labs and are always on hand to provide assistance.
The course is taught at the School of Computer Science and Mathematics.
The School of Computer Science and Mathematics is driven by the philosophy of 'learning through making'; we focus strongly on facilitating a hands-on experience, student-led and owned product portfolios and producing industry-ready graduates.
We use a range of innovative teaching and learning approaches in our invigorated and modernised degree programmes; combining studio practices, project-based learning, and context-driven lectures to facilitate an informed approach to problem solving.
Postgraduate students may run or assist in lab sessions and may also contribute to the teaching of seminars under the supervision of the module leader.
Depending on the programme of study, there may be extra costs that are not covered by tuition fees which students will need to consider when planning their studies. Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, access to shared IT equipment and other support services. Accommodation and living costs are not included in our fees.
Where a course has additional expenses, we make every effort to highlight them. These may include optional field trips, materials (e.g. art, design, engineering), security checks such as DBS, uniforms, specialist clothing or professional memberships.
This degree is excellent preparation for a wide variety of careers, such as systems and business analysts, software engineers, programmers and network specialists.
Computing qualifications are amongst the most versatile and enable graduates to find employment in a wide spectrum of careers ranging from systems and business analysts, and software engineers, through to programmers and network specialists in a wide range of public and private sector industries, and AI is one of the fastest-growing areas of computer science as the tools become ubiquitous and industry races to adopt them.
Our curriculum is largely applied in nature with many case studies chosen for their topicality and relevance to industry such as information systems design, programming, networking, and implementation issues. Working on case studies designed to simulate the working environment, typically in teams, gives students experience of applying their skills to real-world problems.
To further set the material in context as well as inspire our students leading practitioners from industry, such as Google and IBM are invited to give guest lectures and workshops.
The school has close links with the British Computer Society which contributes an annual prize for the School's top-performing graduate across their whole course – this could be you!
Placements:
"To be successful, tomorrow's leaders will need to be far more rounded individuals than ever before. They will collaborate in pursuit of shared goals. They will guide, challenge and support...They will have an appetite for change and a hunger for continuous improvement, and they will have an ethos of learning and development..." Jeremy Darroch, Former Chief Executive, Sky.
"Doing a placement year effectively gives you one foot in the door of a future job and to stand out from the crowd... as well as enhancing my CV... and future interviews. It's a great motivator to be successful in my studies as it only serves to open even more doors and gain more skills." Placement student at Jagex Games Studios Ltd.
There is a lot of support available for students looking to secure a placement (e.g. a jobs board with placement vacancies, help with writing CVs and mock interviews). Getting a placement and passing the placement year are ultimately the student's responsibility.
Placements can be with large multinational companies, international companies, local companies and small start-ups; offering a diverse range of posts. Here are some examples of employers and roles:
Construction-based placement employers | Construction-based placement roles |
---|---|
RG Group Multiplex Costain Willmott Dixon Fluor |
Assistant site manager Assistant trades package manager Assistant logistics manager Health and safety officer Construction engineer |
Science-based placement employers | Science-based placement roles |
Reckitt and Benckiser GSK Drug Control Centre Minton Treharne and Davies Ltd Various local and international hospitals |
Bioanalytical sciences Lab assistant Pharmacy assistant Sports coach |
Engineering-based placement employers | Engineering-based placement roles |
Airbus BAM Nuttall Nissan Bosch Wozair |
Analysis of aircraft structure Construction resources specialist Site engineer assistant |
Computing and IS-based placement employers | Computing and IS-based placement roles |
Disney Sony Interactive Entertainment Europe IBM McKinsey Intel |
Database coordinator Software developer Website developer App developer |
Mathematics-based placement employers | Mathematics-based placement roles |
Lloyds Banking Group AXA Allianz PAU Education, Spain |
Analyst Investment solutions Research analyst Accounts assistant |
The scrolling banner(s) below display some key factual data about this course (including different course combinations or delivery modes of this course where relevant).
The information on this page reflects the currently intended course structure and module details. To improve your student experience and the quality of your degree, we may review and change the material information of this course. Course changes explained.
Programme Specifications for the course are published ahead of each academic year.
Regulations governing this course can be found on our website.