Data Science Conversion MSc
Subject and course type
- Postgraduate
Do you have a bachelors degree in a non-computing or mathematics discipline, but want to embrace the opportunities emerging in the rapidly expanding data science field? This Data Science Conversion MSc, accredited by the British Computer Society (BCS), the Chartered Institute for IT, could be the course for you.
This course has flexible entry points and has been designed for a variety of disciplines and backgrounds. If you’re coming from a computing or mathematics discipline, you should head over to our Data Science MSc page.
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Gain the programming and statistics skills you need to pursue a career in data science
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.
At our Penrhyn Road campus, you will have access to a modern environment with the latest equipment, including:
- dedicated postgraduate computing laboratories, fully-equipped with fold-flat LCD screens, data-projection systems and high-spec processors
- industry-standard development software and tools, such as Python, Scikit, Learn and Tensorflow
- the learning resources centre, offering subject libraries, online database subscriptions and resource materials
Our dedicated team of IT technicians support our labs and are always on-hand to provide assistance.
Plus, Kingston is just a 30-minute train journey from central London, where you can access a wealth of additional libraries and archives. These include the British Library and the Institute of Engineering and Technology.
Being given the chance to become one of the few black women entering the tech field is a significant achievement, especially given the lack of diversity in the field. The support from lecturers, the resources, the workshops and the diverse motivated classmates have made it easier. I'm excelling in the course and I’m incredibly grateful that the scholarship gave me the chance to pursue my passion. Transitioning from biomedical science to data science was a challenge I never expected to take on, but here I am, and I'm forever thankful for the opportunity!
Why choose this course
Large data sets are widespread in business, science and government. Consequently, there is an increasing demand for data-savvy professionals, both in industry and in research, who are able to make sense of complex datasets, build models and apply them to the solution of relevant problems.
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 learn to apply them.
Like many MSc courses in the School of Computer Science and Mathematics, Data Science benefits from a diverse community of learners. You will study in week-long blocks that can fit around different work/study patterns. You will also get to take advantage of opportunities for exposure to cutting-edge examples and exercises.
Please note that this course will begin earlier than other Kingston courses, to allow us to deliver a two-week ‘boot camp' of intensive pre-sessional teaching, to equip you with the technical skills needed to complete the course.
Accreditation
This degree has been accredited by the British Computer Society (BCS), the Chartered Institute for IT. Accreditation is a mark of assurance that the degree meets the standards set by BCS. An accredited degree entitles you to professional membership of BCS, which is an important part of the criteria for achieving Chartered IT Professional (CITP) status through the Institute.
Some employers recruit preferentially from accredited degrees, and an accredited degree is likely to be recognised by other countries that are signatories to international accords. This degree is accredited by BCS for the purposes of partially meeting the academic requirement for registration as a Chartered IT Professional.
Course content
The multidisciplinary nature of data science is reflected in this MSc programme. The combination of modules in data management, analysis, modelling, visualisation and artificial intelligence (AI), are taught by a cross-disciplinary team. The team's collective 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.
Please note: You will be required to complete a ‘boot camp' of intensive pre-sessional teaching before starting your course in September: introducing you to baseline programming, statistics, and data modelling.
Modules
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.
Core modules
30 credits
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.
30 credits
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.
30 credits
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.
60 credits
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:
- Select, justify and use effectively the research methods and techniques appropriate for particular cases in order to carry out a literature search and an independent work of research
- Critically identify the need to position their research in the wider academic or business context and structure the dissertation format to agreed conventions
- Plan, manage and critically evaluate the project using the techniques and tools needed in order to bring it in successfully on time and within resourcing limits
- Identify and critically analyse real-world problems or knowledge gaps to which academic concepts and methods can be realistically applied to improve or resolve the problem situation
- Apply skills to show an ability to engage in academic and professional communication with others in their field through report and presentation
- Present critical awareness in applying appropriate legal, social or ethical obligations and when required, respond to the financial and other constraints of a corresponding business environment.
30 credits
This module introduces fundamental concepts and methods in Classical Machine Learning and Pattern Recognition as well as Neural Networks and Deep Learning. You will firstly be introduced to classical methods, before being taught modern approaches. You will then be exposed to applications related to your course. The module is taught in a practical fashion and therefore some knowledge of a programming language is required.
Professional placement
Professional placement
120 credits
The Professional Placement module is a core module if you're following a masters programme that incorporates an extended professional placement. It provides you with the opportunity to apply your knowledge and skills in an appropriate working environment, and develops and enhances key employability and subject specific skills in your chosen discipline. You may wish to use the placement experience as a platform for a major project or your future career.
It is your responsibility 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 your Course Leader prior to commencement to ensure its suitability. You will have access to the standard placement preparation activities offered by the Student Engagement and Enhancement (SEE) group.
Read more about the postgraduate work placement scheme.
Career opportunities
Graduates from this course go on to pursue careers contained within the more generic data science umbrella. For example, they may become data engineers, data analysts and machine learning engineers.
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, talks from industry speakers and interviews on campus. These events give you the opportunity to hear from, and network with, employers in an informal setting.
Teaching and assessment
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. These could be 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, write reports and give presentations. Students will have the opportunity in some assignments to identify topics and target audiences in consultation with teaching staff. This enables them to express their individuality and appreciate the diversity within the course.
As students progress through the course, they are guided and supported to assemble a portfolio of tangible outputs which evidence, explicitly, the knowledge and skills they have gained. This portfolio may be used to demonstrate the students' 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. Typically this 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 ÂÌñ»»ÆÞ, 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.
- Scheduled learning and teaching: 292 hours
- Guided independent study (self-managed time): 1,508 hours
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:
- 94% coursework
- 3% exams and tests
- 3% practical
(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 20–40 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.
Fees and funding
| Fee category | Fee |
|---|---|
| Home (UK students) | |
| Full Time | £12,400 |
| Part Time | £6,820 |
| International | |
| Full Time | £19,300 |
| Part Time | £10,615 |
| Fee category | Fee |
|---|---|
| Home (UK students) | |
| Full Time | £10,900 |
| Part Time | £5,995 |
| International | |
| Full Time | £18,500 |
| Part Time | £10,175 |
Funding support for postgraduate students
If you are a UK student living in England and under 60, you can apply for a loan to study for a postgraduate degree on the government's website.
Scholarships and bursaries
For students interested in studying Data Science Conversion MSc at Kingston, there are several opportunities to seek funding support:
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.
The Inspire the Future Scholarship offers a 40% reduction in fees for taught masters or postgraduate diploma courses with September start dates. 20 scholarships are available for progressing ÂÌñ»»ÆÞ graduates. For more information on how to apply for this scholarship, visit the Inspire the Future Scholarship page.
International postgraduate students could receive up to £5,000 towards tuition in their first year of study.
For more information on how to apply for these scholarships, visit the International Scholarship page.
If you are a ÂÌñ»»ÆÞ 2024/25 undergraduate progressing to a 2025/26 postgraduate degree (taught or research), you could get a 15% reduction in tuition fees.
For more information on how to apply for this scholarship, visit the Postgraduate Progression Scholarship page.
ÂÌñ»»ÆÞ offers a 10% discount on full and part-time postgraduate degree course tuition fees to our alumni.
For more information on how to apply for this discount, visit our alumni discount page.
Additional course costs
Some courses may require additional costs beyond tuition fees. When planning your studies, you’ll want to consider tuition fees, living costs, and any extra costs that might relate to your area of study.
Your tuition fees include costs for teaching, assessment and university facilities. So your access to libraries, shared IT resources and various student support services are all covered. Accommodation and general living expenses are not covered by these fees.
Where applicable, additional expenses for your course may include:
Our libraries have an extensive collection of books and journals, as well as open-access computers and laptops available to rent. However, you may want to buy your own computer or personal copies of key textbooks. Textbooks may range from £50 to £250 per year. And a personal computer can range from £100 to £3,000 depending on your course requirements.
While most coursework is submitted online, some modules may require printed copies. You may want to allocate up to £100 per year for hard-copies of your coursework. It’s worth noting that 3D printing is never compulsory. So if you choose to use our 3D printers, you’ll need to pay for the material. This ranges from 3p per gram to 40p per gram.
ÂÌñ»»ÆÞ will pay for all compulsory field trips. Fees for optional trips can range from £30 to £350 per trip.
Your tuition fees don’t cover travel costs. To save on travel costs, you can use our free intersite bus service. This route links the campuses and halls of residence with local train stations - Surbiton, Kingston upon Thames, and Norbiton.
How to apply
Before you apply
Please read the entry criteria carefully to make sure you meet all requirements before applying.
How to apply online
Use the course selector drop down at the top of this page to choose your preferred course, start date and mode, then click 'Apply now'. You will be taken to our Online Student Information System (OSIS) where you will complete your application.
If you’re starting a new application, you’ll need to select ‘new user’ and set up a username and password. This will allow you to save and return to your application.
Application deadlines
We encourage you to apply as soon as possible. Applications will close when the course is full.
Information required to confirm your place
If English is not your first language, we will require proof of your proficiency to allow us to confirm your place on the course. This will generally be either an IELTS or TOEFL test certificate, which can be forwarded to us after you have submitted your application. If you do not hold a formal English language qualification, please indicate how you have acquired your proficiency in written and spoken English.
After you have applied
If the postgraduate admissions tutor requires further information or wishes to invite you to further assessment by interview they will contact you directly. You will then hear whether your application has been successful.
If you do not clearly meet the standard entry requirements and the admission tutor wishes to see a portfolio from you, you will be sent an email asking you to upload your portfolio to your ÂÌñ»»ÆÞ OSIS account. Further details on how to do this will be provided at the time.
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. Find out more about course changes
for the course are published ahead of each academic year.
Regulations governing this course can be found on our website.
