Computer Science and Artificial Intelligence with Foundation
Kingston University
UCAS Code: I102 | Bachelor of Science (with Honours) - BSc (Hons)
Entry requirements
A level
Any subject considered.
A pass in any Access Course would be deemed suitable for entry onto this programme.
GCSE/National 4/National 5
GCSE Mathematics with grade 6 or above.
Pearson BTEC Level 3 National Extended Diploma (first teaching from September 2016)
Any subject considered.
T Level
UCAS Tariff
UCAS points from a minimum of 2 A-Levels or equivalent Level 3 qualifications.
About this course
**Reasons to choose Kingston**
- 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.
- Our commitment to high quality teaching has been recognised with a Teaching Excellence Framework (TEF) Gold rating. The University has received an overall rating of Gold, as well as securing a Gold award in the framework's two new student experience and student outcomes categories.
**About this course**
The course is ideal for students who are interested in developing and applying problem-solving skills to real-world problems and 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 an understanding of the wider socio- technical implications.
**Future Skills**
Embedded within every course curriculum and throughout the whole Kingston experience, Future Skills will play a role in shaping you to become a future-proof graduate, providing you with the skills most valued by employers such as problem-solving, digital competency, and adaptability.
As you progress through your degree, you'll learn to navigate, explore and apply these graduate skills, learning to demonstrate and articulate to employers how future skills give you the edge.
At Kingston University, we're not just keeping up with change, we're creating it.
**Career opportunities**
This degree is excellent preparation for a wide variety of careers, such as a data analyst, machine learning engineer, systems and business analyst, software engineer, programmer and network specialist.
Modules
Example modules
– Principles of Data Analytics for AI
– Applied AI and Machine Learning
– Mobile Application Development
To view the full list of modules, please visit the University course webpage.
Assessment methods
Assessment typically comprises exams (eg test or exam), practical (eg presentations, performance) and coursework (eg essays, reports, self-assessment, portfolios, dissertation).
Tuition fees
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Extra funding
*
The Uni
Kingston University
Department of Computer Science
What students say
We've crunched the numbers to see if overall student satisfaction here is high, medium or low compared to students studying this subject(s) at other universities.
How do students rate their degree experience?
The stats below relate to the general subject area/s at this university, not this specific course. We show this where there isn’t enough data about the course, or where this is the most detailed info available to us.
Artificial intelligence
Sorry, no information to show
This is usually because there were too few respondents in the data we receive to be able to provide results about the subject at this university.
Who studies this subject and how do they get on?
Most popular A-Levels studied (and grade achieved)
Computer science
Teaching and learning
Assessment and feedback
Resources and organisation
Student voice
Who studies this subject and how do they get on?
Most popular A-Levels studied (and grade achieved)
After graduation
The stats in this section relate to the general subject area/s at this university – not this specific course. We show this where there isn't enough data about the course, or where this is the most detailed info available to us.
Artificial intelligence
What are graduates doing after six months?
This is what graduates told us they were doing (and earning), shortly after completing their course. We've crunched the numbers to show you if these immediate prospects are high, medium or low, compared to those studying this subject/s at other universities.
Top job areas of graduates
Artificial intelligence is a very specialist subject taken by less than 100 people a year at the moment, so there is little reliable information available on graduate prospects - bear that in mind when you review the stats above. Graduates taking this type of subject are more likely than other computing graduates to go into further research. However, if you want to find out more specifically about the potential graduate outcomes of a specific course, it's a good idea to go on open days and talk to tutors about what previous graduates have gone on to do.
Computer science
What are graduates doing after six months?
This is what graduates told us they were doing (and earning), shortly after completing their course. We've crunched the numbers to show you if these immediate prospects are high, medium or low, compared to those studying this subject/s at other universities.
Top job areas of graduates
This is a newly-classified subject area for this kind of data, so we don’t currently have very much information to display or analyse yet. The subject is linked to important and growing computing industries, and over time we can expect more students to study them — there could be opportunities that open up for graduates in these subjects as the economy develops over the next few years.
What about your long term prospects?
Looking further ahead, below is a rough guide for what graduates went on to earn.
Artificial intelligence
The graph shows median earnings of graduates who achieved a degree in this subject area one, three and five years after graduating from here.
£22k
£29k
£30k
Note: this data only looks at employees (and not those who are self-employed or also studying) and covers a broad sample of graduates and the various paths they've taken, which might not always be a direct result of their degree.
Computer science
The graph shows median earnings of graduates who achieved a degree in this subject area one, three and five years after graduating from here.
£22k
£29k
£30k
Note: this data only looks at employees (and not those who are self-employed or also studying) and covers a broad sample of graduates and the various paths they've taken, which might not always be a direct result of their degree.
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Teaching Excellence Framework (TEF):
We've received this information from the Department for Education, via Ucas. This is how the university as a whole has been rated for its quality of teaching: gold silver or bronze. Note, not all universities have taken part in the TEF.
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This information comes from the National Student Survey, an annual student survey of final-year students. You can use this to see how satisfied students studying this subject area at this university, are (not the individual course).
This is the percentage of final-year students at this university who were "definitely" or "mostly" satisfied with their course. We've analysed this figure against other universities so you can see whether this is high, medium or low.
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This information is from the Higher Education Statistics Agency (HESA), for undergraduate students only.
You can use this to get an idea of who you might share a lecture with and how they progressed in this subject, here. It's also worth comparing typical A-level subjects and grades students achieved with the current course entry requirements; similarities or differences here could indicate how flexible (or not) a university might be.
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Post-six month graduation stats:
This is from the Destinations of Leavers from Higher Education Survey, based on responses from graduates who studied the same subject area here.
It offers a snapshot of what grads went on to do six months later, what they were earning on average, and whether they felt their degree helped them obtain a 'graduate role'. We calculate a mean rating to indicate if this is high, medium or low compared to other universities.
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Graduate field commentary:
The Higher Education Careers Services Unit have provided some further context for all graduates in this subject area, including details that numbers alone might not show
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The Longitudinal Educational Outcomes dataset combines HRMC earnings data with student records from the Higher Education Statistics Agency.
While there are lots of factors at play when it comes to your future earnings, use this as a rough timeline of what graduates in this subject area were earning on average one, three and five years later. Can you see a steady increase in salary, or did grads need some experience under their belt before seeing a nice bump up in their pay packet?
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