Data Science MSc
Why choose this course?
Data Science is one of the most rapidly expanding areas of employment globally, due to rapid and ongoing developments in computer systems and data gathering. Large data sets are widespread in business, science and government.
This course builds on the established strengths of the Mathematics and Computer Science programmes and develops a multidisciplinary approach to the computational analysis of data. Contemporary society faces new challenges in the analysis of data, predictive analytics in support of decision making processes that are both mathematical and computational. There is an increasing demand for data-savvy professionals both in industry and in research who are able make sense of large amounts of data and apply it to the solution of relevant problems.
This course offers postgraduates with some background in computing, mathematics, or data-based investigation the opportunity to develop their skills in a way which will prepare them for careers in this fast-growing and exciting area which spans virtually all areas of commerce and industry as well as scientific research, and involves working with individuals and organisations to extract value from the ever-increasing volume of data that is available.
| Mode | Duration | Start date |
|---|---|---|
| Full time | 1 year | September 2020, January 2021, March 2021 |
| Full time | 2 years including professional placement | September 2020, January 2021, March 2021 |
| Part time | 2 years | September 2020, January 2021, March 2021 |
| Location | Penrhyn Road |
Reasons to choose Kingston University
- Data Science aligns with a thriving area of applied machine learning research in the School of Computer Science & Mathematics - giving opportunities for exposure to plenty of cutting edge examples and exercises.
- The Data Science course is designed for entry from a variety of disciplines and backgrounds, with more flexible entry-points than the more commonplace style of "September-starter" postgraduate course.
- Like many MSc courses in the School of Computer Science and Mathematics, Data Science benefits from broad entry requirements, a diverse community of learners, and study in week-long blocks that can fit around different work/study patterns.
What you will study
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.
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
Databases and Data Management
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.
Data Analytics and Visualisation
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.
Applied Data Programming
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.
Machine Learning and Artificial Intelligence
30 credits
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.
Project Dissertation
60 credits
This module constitutes the major individual piece of work of the masters programme where you will carry out a project involving independent critical research, design and implementation (where applicable).
On successful completion of the module, you 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 your 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.
Professional Placement
120 credits
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.
The information above reflects the currently intended course structure and module details. Updates may be made on an annual basis and revised details will be published through Programme Specifications ahead of each academic year. The regulations governing this course are available on our website. If we have insufficient numbers of students interested in an optional module, this may not be offered.
Work placement scheme
Many postgraduate courses at Kingston University allow students to do 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 work placement, just the opportunity to undertake it. As the work placement is an assessed part of the course, it is covered by a student's tier 4 visa.
Invoicing on the placement courses occurs in two stages. The standard course fee is payable in Year 1 with the placement fee invoiced in Year 2. Therefore, students starting in September 2020 would therefore be charged the placement fee of £1,385 in September 2021. Students commencing the course in September 2021 will be invoiced the placement fee in 2022 (fee tbc).
This amount will only be charged to your account after you find a placement and are enrolled on the module. You will not be charged this fee if you do not manage to secure a work placement.
Find out more about the postgraduate work placement scheme.
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, 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.
Who teaches this course
This course is delivered by the School of Science and Mathematics in the Faculty of Science, Engineering and Computing.
The Faculty's wide selection of undergraduate and postgraduate courses covers a diverse range of subject areas, from aerospace to geography; from maths and computing to biotechnology; and many more. Our collaborative set-up provides new opportunities for our students, and we design our courses with industry professionals to ensure you stay up to date with the latest developments.
School of Computing and Mathematics
The School of Computer Science and Mathematics offers high-quality undergraduate and postgraduate courses, designed to reflect the developing needs of business and industry. We deliver our teaching in an exciting and challenging learning environment, and make use of modern, well-equipped facilities.
Kingston University offers high quality undergraduate and postgraduate courses focused on applied computer science and mathematics, cyber security, games and digital media - designed to reflect the developing needs of business and industry. They are underpinned by the latest technologies and strengthened by active collaborations with industry leaders providing practical experience through placements as well as educational partnerships.
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.
Our modern teaching environment at Kingston University
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:
- dedicated postgraduate computing laboratories, fully-equipped with fold-flat LCD screens, data-projection systems and high-spec processors
- development software and tools, such as Linux, Microsoft.net, Dreamweaver MX, Flash 8, Eclipse, Java 2 Standard and Mobile Editions, tools for Motorola and Nokia phones, UML and CASE tools and NXP Processors Development Kits
- Digital Signal Processors (dsPIC Digital Signal Controllers)
- IP Set Top Box development environment (NXP's STB810)
- Electronics Laboratory
- a mix of wireless LAN technologies
- the learning resources centre, offering subject libraries, online database subscriptions and resource materials
- a postgraduate teaching suite
- the dedicated Graduate Centre on campus, providing seminar rooms and social spaces.
Our dedicated team of IT technicians support the labs and are always on hand to provide assistance.
Resources in London
Kingston is just a 30-minute train journey away from central London. Here you can access a wealth of additional libraries and archives, including the British Library and the Institute of Engineering and Technology.
Course fees and funding
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 require a Tier 1-5 visa to reside in the UK (this includes a Tier 4 student visa), you may not be able to enrol on a part-time programme at the University.
Kingston University has carefully considered the Tier 4 visa route and has decided not to offer Tier 4 part-time study. Tier 4 sponsorship is only available to students studying on a full-time course.
Careers and recruitment advice
The Faculty of Science, Engineering and Computing 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.
After you graduate
The new Data Science course is aimed at careers contained within the more generic 'Data Science' umbrella, including Data Engineer, Data Analyst and Machine Learning Engineer.
