Computer Science and Artificial Intelligence BSc (Hons)

Why choose this course?

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

Reasons to choose Kingston University

  • To set the material in context as well as inspire our students, we invite leading practitioners from industry, such as Google and IBM, to give guest lectures and workshops.
  • There's the opportunity for a year's work placement. This will give you valuable experience and help prepare you for a career in finance or data analysis.
  • You'll use applications that model the real world and industry-standard software such as Python R, Matlab and SAS

What you will study

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.

Year 1

Year 2

Year 3

This first year provides a broad exposure to the essential domain topics: computing fundamentals, programming, professional practice and mathematics for artificial intelligence.

Core modules

Mathematics for AI

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.

Programming I: Thinking Like a Programmer

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.

Computing Fundamentals

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.

Professional Environments 1

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.

Core modules

Artificial Intelligence Fundamentals

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.

Data Modelling

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:

  • Design a conceptual data model that captures an organisation's data requirements for a database.
  • Convert conceptual models to physical models (e.g. relational data model) and apply normalisation.
  • Build efficient and effective databases using SQL and write queries to retrieve data.
  • Articulate the role of data administration, security and recovery processes in database management systems.
Principles of Data Analytics for AI

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.

Professional Environments 2

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.

Optional modules

Programming II - Software Development

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.

Computing Systems

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.

Core modules

Applied AI and Machine Learning

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:

  • Demonstrate understanding of and skills in applying advanced A.I. methodologies
  • Discuss the issues in problem domains such as Computer Vision, Natural Language Processing and Anomaly Detection which make them unsuitable for solution using traditional methods alone, and how A.I. can address these.
  • Identify, apply and implement appropriate A.I. approaches for solving problems from those domains studied
  • Critically evaluate and discuss to what extent use of such approaches have been successful, including their legal, ethical, societal implications, and suggest alternative solutions when appropriate
Advanced Data Modelling

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.

Individual Project

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:

  • Carry out a literature search to summarise and evaluate background work relevant to the chosen course, undertake an investigation of the planned topic and critically evaluate the outcomes.
  • Plan tasks within time and other commitment constraints.
  • Produce a well-structured written report demonstrating a sound understanding of the theory of the chosen project area including relevant references and use of language, diagrams, tables and graphs where necessary.
  • Present or answer questions clearly and concisely in a structured interview about their work.
  • Create an artefact relating to the chosen course.
  • Identify and take account of relevant legal, social and ethical issues.

Optional modules

Software Development Practice

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.

User Experience Design Thinking

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.

Mobile Application Development

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.

Programming III- Patterns and Algorithms

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.

Data Analytics for AI

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.

Bayesian Estimation and Risk Modelling

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.

Business Modelling with AI

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.

Please note

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.

Entry requirements

Typical offer 2023

UCAS tariff points: 112-128 for BSc (Hons); 32 for BSc (Hons) including foundation year from Level 3 qualifications.

A-Levels, BTEC Extended Diploma with grades DMM or BTEC Diploma with grades D*D* in computing, science, engineering or mathematics subject area.

Mathematics GCSE at Grade 6 or above is required.

Candidates are normally required to hold five GCSE subjects at grade C/4 or above, including English Language.

Alternative routes

We will consider a range of alternative Level 3 qualifications such as an Access Course in a relevant Science, Computing, Maths or Engineering subject which has been passed with 112 UCAS points.

Applications from those that have undertaken a Computing foundation year will also be considered.


We welcome applications from International Applicants. View our standard entry requirements from your country.

All non-UK applicants must meet our English language requirements. For this course it is Academic IELTS of 6.0, with no element below 5.5.

Country-specific information

You will find more information on country specific entry requirements in the International section of our website.

Find your country:

Typical offer and UCAS points explained

Like most universities, we use the UCAS Tariff point system for our course entry requirements.

Find out more about UCAS Tariff points and see how A-level, AS level, BTEC Diploma and T-level qualifications translate to the points system.

Teaching and assessment

Guided independent study (self-managed time)

When not attending timetabled sessions, you will be expected to continue learning independently through self-study. This typically will involve reading journal articles and books, working on individual and group projects, undertaking preparing coursework assignments and presentations, and preparing for final assignments. Your independent learning is supported by a range of excellent facilities including online resources, the library and CANVAS, the online virtual learning platform.

Academic support

Our academic support team here at Kingston University provides help in a range of areas.

Dedicated personal tutor

When you arrive, we'll introduce you to your personal tutor. This is the member of academic staff who will provide academic guidance, be a support throughout your time at Kingston and who will show you how to make the best use of all the help and resources that we offer at Kingston University.

Your workload

Type of learning and teaching

Year 1

Year 2

Year 3

Year 1
  • Scheduled learning and teaching: 376 hours
  • Guided independent study (self-managed time): 824 hours
Year 2
  • Scheduled learning and teaching: 282 hours
  • Guided independent study (self-managed time): 618 hours
Year 3
  • Scheduled learning and teaching: 203 hours
  • Guided independent study (self-managed time): 697 hours


  • For Years 1 and 2, 31% of your time is spent in timetabled learning and teaching activity.
  • For Year 3, 23% of your time is spent in timetabled learning and teaching activity.
  • Contact hours may vary depending on your modules. 

How you will be assessed

Assessment typically comprises exams (e.g. test or exam), practical (e.g. presentations, performance) and coursework (e.g. essays, reports, self-assessment, portfolios, dissertation). The approximate percentage for how you will be assessed on this course is as follows, though depends to some extent on the option modules you choose.

Type of assessment

Year 1

Year 2

Year 3

Year 1
  • Coursework: 59%
  • Exams: 41%
Year 2
  • Coursework: 63%
  • Exams: 23%
  • Practical: 14%
Year 3
  • Coursework: 68%
  • Exams: 23%
  • Practical: 9%

Feedback summary

We aim to provide feedback on assessments within 20 working days.

Your timetable

Your individualised timetable is normally available to students within 48 hours of enrolment. Whilst we make every effort to ensure timetables are as student-friendly as possible, scheduled learning and teaching can take place on any day of the week between 9am and 6pm. For undergraduate students, Wednesday afternoons are normally reserved for sports and cultural activities, but there may be occasions when this is not possible. Timetables for part-time students will depend on the modules selected.

Class sizes

To give you an indication of class sizes, this course normally enrols 140 students and lecture sizes are normally 140-290. However this can vary by module and academic year.


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:

  • computing laboratories - fully equipped with fold-flat LCD screens, data-projection systems and high-spec processors;
  • state-of-the-art hardware and the latest software, including:
    • development software and tools - such as Linux,, Dreamweaver, Flash 11, Eclipse, Java 2 Standard and Mobile Editions, tools for Motorola and Nokia phones, UML and CASE tools and NXP Processors Development Kits;
    • Maple, Matlab and SAS (mathematics and statistics software packages used by corporations, governments, universities, etc. across the globe);
    • Digital Signal Processors (dsPIC Digital Signal Controllers);
    • a mix of wireless LAN technologies; and
    • subject libraries, online database subscriptions and resource materials.
  • Our dedicated team of IT technicians support the labs and are always on hand to provide assistance.

Who teaches this course?

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.

Course fees and funding

2023/24 fees for this course

The tuition fee you pay depends on whether you are assessed as a 'Home' (UK), 'Islands' or 'International' student. In 2023/24 the fees for this course are:

 Fee category Amount
Home (UK students) £9,250*
Foundation Year: TBA**

Year 1 (2023/24): £15,800
Year 2 (2024/25): £16,200
Year 3 (2025/26): £16,600
Year 4 (2026/27): £17,000

For courses with a sandwich year, the fee for the placement year can be viewed on the undergraduate fees table. The placement fee published is for the relevant academic year stated in the table. This fee is subject to annual increases but will not increase by more than the fee caps as prescribed by the Office for Students or such other replacing body.

* For full-time programmes of a duration of more than one academic year, the published fee is an annual fee, payable each year, for the duration of the programme. Your annual tuition fees cover your first attempt at all of the modules necessary to complete that academic year. A re-study of any modules will incur additional charges calculated by the number of credits. Home tuition fees may be subject to annual increases but will not increase by more than the fee caps as prescribed by the Office for Students or such other replacing body. Full-time taught International fees are subject to an annual increase and are published in advance for the full duration of the programme.

Eligible UK students can apply to the Government for a tuition loan, which is paid direct to the University. This has a low interest-rate which is charged from the time the first part of the loan is paid to the University until you have repaid it.

** Foundation fees are awaiting the outcomes of the Government's 'Higher education policy statement and reform consultation'.

Note for EU students: UK withdrawal from the European Union

The Government has recently announced that new students from the European Union and Swiss Nationals starting their course after August 2021 will no longer be eligible for a student loan in England for Undergraduate or Postgraduate studies from the 2021/22 academic year. This decision only applies to new EU students starting after 2021/22. If you are an existing/continuing EU student, you will continue to be funded until you graduate or withdraw from your course.

Need to know more?

Our undergraduate fees and funding section provides information and advice on money matters.

Additional costs

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.


Our libraries are a valuable resource with an extensive collection of books and journals as well as first-class facilities and IT equipment. You may prefer to buy your own copy of key textbooks, this can cost between £50 and £250 per year.

Computer equipment

There are open-access networked computers available across the University, plus laptops available to loan. You may find it useful to have your own PC, laptop or tablet which you can use around campus and in halls of residences. Free WiFi is available on each of the campuses. You may wish to purchase your own computer, which can cost between £100 and £3,000 depending on your course requirements.

Photocopying and printing

In the majority of cases written coursework can be submitted online. There may be instances when you will be required to submit work in a printed format. Printing, binding and photocopying costs are not included in your tuition fees, this may cost up to £100 per year.


Travel costs are not included in your tuition fees but we do have a free intersite bus service which links the campuses, Surbiton train station, Kingston upon Thames train station, Norbiton train station and halls of residence.


If the placement year option is chosen, during this year travel costs will vary according to the location of the placement, and could be from £0 to £2,000.

Field trips

All field trips that are compulsory to attend to complete your course are paid for by the University. There may be small fees incurred for optional field trips such as travel costs and refreshments.

After you graduate

This degree is excellent preparation for a wide variety of careers, such as systems and business analysts, software engineers, programmers and network specialists.

Examples of recent graduate destinations

  • Solutions architect
  • Software engineer
  • User experience designer
  • Usability engineer
  • System analyst
  • Technical analyst
  • Data Analyst
  • Security analysis
  • Data scientist
  • Business analysis
  • Business intelligence analysts
  • Software developer
  • System support manager
  • Software administrator
  • IT consultant
  • IT developer
  • Database administrator
  • Network support executive
  • Internet developer
  • Project manager
  • Web master
  • Analyst programmer
  • Web designer
  • Network analyst

Careers and recruitment advice

The Faculty has a specialist employability team. It provides friendly and high-quality careers and recruitment guidance, including advice and sessions on job-seeking skills such as CV preparation, application forms and interview techniques. Specific advice is also available for international students about the UK job market and employers' expectations and requirements.

The team runs employer events throughout the year, including job fairs, key speakers from industry and interviews on campus. These events give you the opportunity to hear from, and network with, employers in an informal setting.

Employability preparation at Kingston University

In addition to building expertise in your own discipline, our courses will also help you to develop key transferable skills that you'll need for professional life or further study once you graduate.

As well as a range of careers and employability activities at Kingston, we also offer you the chance to apply and develop your skills in live contexts as an integral part of your course. Opportunities include:

  • placements;
  • working or studying abroad;
  • volunteering;
  • peer mentoring roles; and
  • internship opportunities within and outside the University.

In your final year, you'll get the opportunity to complete a major 'capstone' project where you can apply the knowledge and skills you have acquired to a range of real issues in different contexts. This is a great way to learn and is a valuable bridge to employment or further research at masters level.

Courses available after you graduate

If you decide that you would like to go on to postgraduate study after your undergraduate course, we offer a 10% discount on our postgraduate course tuition fees to our alumni.

Links with business and industry

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!

Work placement year

How you can work in industry during your course


  • provide work experience that is relevant to your course and future career
  • improve your chances of graduating with a higher-grade degree
  • enhance your CV
  • lead to a graduate job
  • enable you to earn a year's salary whilst studying (the vast majority of placements are paid)
  • help you to select your final-year project.

"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.

Examples of placements

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 employersConstruction-based placement roles 
RG Group
Willmott Dixon
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
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
BAM Nuttall
Analysis of aircraft structure
Construction resources specialist
Site engineer assistant
Computing and IS-based placement employersComputing and IS-based placement roles
Sony Interactive Entertainment Europe
Database coordinator
Software developer
Website developer
App developer
Mathematics-based placement employersMathematics-based placement roles
Lloyds Banking Group
PAU Education, Spain
Investment solutions
Research analyst
Accounts assistant

Key information set

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).

Course changes and regulations

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.