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Data Science is one of the most rapidly expanding areas of employment globally, due to fast-paced and ongoing developments in computer systems and data gathering. Large data sets are widespread in business, science and government.
This conversion course is part of the government's response to the shortage of data science and artificial intelligence specialists in the UK. It aims to help students without strong backgrounds in programming or statistics to acquire a level of skill in these areas and learning to apply them.
Please note that this course will begin earlier than other Kingston courses, to allow us to deliver a two-week ‘bootcamp' of intensive pre-sessional teaching, to equip you with the technical skills needed to complete the course.
To be eligible for this conversion course, students must meet the nationality or residence status criteria to qualify for a postgraduate masters loan.
|Full time||1 year||September 2023|
|Part time||2 years||September 2023|
These scholarships will support 13 home students starting in September 2023, with a bursary of £10,000 each for those converting from a non-computing/maths background via our summer "boot camp" to study the MSc in Data Science.
These scholarships are intended to increase the proportions of traditionally under-represented groups in Data Science and Artificial Intelligence (AI).
For more information, visit the OfS Scholarships for MSc Data Science page.
Those that receive a scholarship will also still be eligible to apply for a postgraduate loan. For more information, read the postgraduate loan information on the government's website.
The multidisciplinary nature of data science is reflected in this MSc programme through the careful combination of modules in data management, analysis, modelling, visualisation and artificial intelligence (AI), which are taught by a cross-disciplinary team whose expertise encompasses mathematics, statistics, AI and machine learning, information management, and user experience design.
For a student to go on placement they are required to pass every module first time with no reassessments. It is the responsibility of individual students to find a suitable paid placement. Students will be supported by our dedicated placement team in securing this opportunity.
The programme is made up of four modules each worth 30 credit points plus an individual project worth 60 credits. The optional Professional Placement can be undertaken following completion of the other modules. The optional Professional Placement taken during an additional year will give 120 credits.
Please note that this is an indicative list of modules and is not intended as a definitive list.
In this module students will be introduced to the methods, techniques and tools that organisations use to collect, manage, store and secure data. Different approaches and methods will be explored to model data requirements using structured and unstructured databases. Students will also be introduced to data warehousing architectures and concepts in "big data". Essential knowledge of data security issues, including policies, structures and practices used to ensure data security and confidentiality, and the way that such issues are addressed in practice, is also examined.
This module introduces the core concepts of data analytics, starting from elementary statistics applied to data-driven decision making, progressing through more sophisticated software-supported data analysis to the presentation of information and its persuasive effect, with applications to business strategy, demographics and social analytics.
This module emphasises a practical and applied approach to programming and software skills for data scientists which differs from typical software engineering approaches in that the emphasis is on the use and manipulation of data using languages and platforms designed for use in real-life, data-driven problems. The languages and platforms are considered only as far as their use for data manipulation are needed with limited exploration of underlying theory or data structures. This prioritises practical implementation including locating, accessing, loading, manipulating, securing, storing and describing data, and enables the introduction of aspects of data analysis, data-mining and machine learning provided by the chosen languages and platforms.
The module introduces fundamental concepts and methods in Machine Learning and Pattern Recognition, and discusses their applications in disciplines such as image and video analysis, computer games, information security, data science and mechatronics. Students are firstly introduced to classical methods, before they are taught modern state-of-the-art approaches. Then, they are exposed to applications related to their course. The module is taught in a practical fashion and therefore some knowledge of a programming language is required.
This module constitutes the major individual piece of work of the Masters Programme where the student carries out a project involving independent critical research, design and implementation (where applicable).
On successful completion of the module, students will be able to:
The Professional Placement module is a core module for those students following a masters programme that incorporates an extended professional placement. It provides students with the opportunity to apply their knowledge and skills in an appropriate working environment and develops and enhances key employability and subject specific skills in their chosen discipline. Students may wish to use the placement experience as a platform for the major project or future career.
It is the responsibility of individual students to find and secure a suitable placement opportunity. This should not normally involve more than two placements which must be completed over a minimum period of 10 months and within a maximum of 12 months. The placement must be approved by the Course Leader, prior to commencement to ensure its suitability. Students seeking placements will have access to the standard placement preparation activities offered by Student Engagement and Enhancement (SEE) group.
Read more about the postgraduate work placement scheme.
Please note: You will be required to complete a ‘bootcamp' of intensive pre-sessional teaching before starting your course in September: introducing you to baseline programming, statistics, and data modelling.
Many postgraduate courses at Kingston University allow students to take the option of a 12-month work placement as part of their course. The responsibility for finding the work placement is with the student; we cannot guarantee the placement, just the opportunity to undertake it. You may find securing a professional placement difficult as they are highly competitive and challenging, but they are also incredibly rewarding. It is very important to prepare and apply yourself if this is the route you wish to take. Employers look for great written and oral communication skills and an excellent CV/portfolio.
Find out more about the postgraduate work placement scheme.
A 2.1 or above honours degree. This can be in any discipline.
Please note: each application is assessed on an individual basis and may be subject to additional requirements, such as undertaking short course(s) and work experience. Meeting particular minimum entry requirements does not automatically guarantee a place.
To be eligible for conversion course students must meet the nationality or residency status criteria to qualify for the postgraduate masters loan.
Due to the nature of the course, we are unable to sponsor students for a student visa. Therefore international applicants or applicants who require a visa to study in the UK would not be eligible.
The learning, teaching and assessment strategies reflect the programme aims and learning outcomes, student background, potential employer requirements, and the need to develop a broad range of technical skills with the ability to apply them appropriately.
The use of coursework emphasises more authentic assessments, which could be, for example, from business or research contacts in local SMEs or colleagues working with "big data" in the NHS, with appropriate ethical and IP approval, as necessary. For example, students will typically create applications, documentation and visualisations, writing reports and giving presentations. Students will have the opportunity in some assignments to identify topics and target audiences in consultation with teaching staff which allows them to express their individuality and appreciate the diversity within course. In this way, as they progress through the course, students are guided and supported to assemble a portfolio of tangible outputs which evidence, explicitly, the knowledge and skills they have gained and which may be used to demonstrate their capabilities to future employers in a format that can be influenced by the students' own preferences.
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, coursework assignments and presentations, and preparing for exams. Your independent learning is supported by a range of excellent facilities including online resources, the library and CANVAS, the online virtual learning platform.
As a student at Kingston University, we will make sure you have access to appropriate advice regarding your academic development. You will also be able to use the University's support services.
Year 1: 15% of your time is spent in timetabled learning and teaching activity.
Contact hours may vary depending on your modules.
Assessment typically comprises in-class tests, practical (e.g. presentations, demonstrations) 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:
(repeat for each year, if part time)
We aim to provide feedback on assessments within 20 working days.
You will be part of an intimate cohort of students which provides dedicated academic guidance and advice as well as the opportunity to build a life-long network of colleagues. Some modules are common across other postgraduate programmes; you may therefore be taught alongside postgraduates from other courses.
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 utilise a range of innovating 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.
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 supports the labs and is always on-hand to provide assistance.
Kingston is just a 30-minute train journey from central London, where you can access a wealth of additional libraries and archives, including the British Library and the Institute of Engineering and Technology.
Here you can find more details about fees for this course, as well as any funding opportunities available to you for this course. Please note that fees relate to the academic year in question and will increase in future years.
If you start your second year straight after Year 1, you will pay the same fee for both years.
If you take a break before starting your second year, or if you repeat modules from Year 1 in Year 2, the fee for your second year may increase.
If you are a UK student, resident in England and are aged under the age of 60, you will be able to apply for a loan to study for a postgraduate degree. For more information, read the postgraduate loan information on the government's website.
Kingston University offers a range of postgraduate scholarships, including:
We also offer the following discounts for Kingston University alumni:
The scholarships will support 13 home students starting in September 2023, with a bursary of £10,000 each for those converting from a non-computing/maths background via our summer "boot camp" to study the MSc in Data Science.
These scholarships are funded by the Office for Students, Carbon Trade Exchange (CTX) and Kingston University and are intended to increase the proportions of traditionally under-represented groups in Data Science and Artificial Intelligence (AI).
For more information, visit the OfS Scholarships for MSc Data Science page.
The Kevin Walsh Scholarship is a one-year scholarship for a taught masters course in the School of Computer Science & Mathematics. It covers the cost of the home fees for a masters degree as well as providing a maintenance grant.
For more information, visit the Kevin Walsh Scholarship page.
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.
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.
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.
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.
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.
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.
This course offers an excellent foundation aimed at careers contained within the more generic data science umbrella, including data engineer, data analyst and machine learning engineer.
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.