Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Health Data Science at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).
Healthcare, with an already established strong relationship with Information & Communication Technologies (ICT), is continuously expanding the knowledge forefront as new methods of acquiring data concerning the health of human beings are developed.
Processing this data to extract valuable information about a population (epidemiological applications) or the individual (personalised healthcare applications) is the work of health data scientists. Their work has the potential to improve quality of life on a large scale.
Swansea University is the first institution in the UK to offer this taught master's programme in Health Data Science designed to develop the essential skills and knowledge required of the Health Data Scientist.
- A one year full-time taught master's programme designed to develop the essential skills and knowledge required of the Health Data Scientist.
- The Health Data Science course is also available for three years part-time study.
- An integrated programme of studies tailored to the essential skill set required for Data Scientists operating within healthcare organisations covering key topics in computation, data modeling, visualisation, machine learning and key methodologies in the analysis of linked health data.
- Hands on experiential learning from the professionals behind the Secure Anonymised Information Linkage (SAIL) Databank, a UK-exemplar project for the large scale mining of healthcare data within a secure environment.
- Strong collaboration links with colleagues from the Centre for Health Services Research of the University of Western Australia, a group of leading experts in the analysis of linked health data.
- The Health Data Science course is based within the award winning Centres for Excellence for Administrative Data and eHealth Research of Swansea University, awarded by the Economic and Social Research Council (ESRC) and Medical Research Council (MRC), enhancing the quality of the course.
The Health Data Science course is suitable for those working in healthcare with roles involving the analysis of health data and also computer scientists with experience in working with data from the healthcare domain, as well as biomedical engineers and other similar professions.
Students must complete 6 modules of 20 credits each and produce a 60 credits dissertation on a Health Data Science project. Each module of the programme requires a short period of attendance that is augmented by preparatory and reflective material supplied via the course website before and after attendance.
Health Data Science students are required to attend the University for 1 week (5 consecutive days) for each module in Part One. Attendance during Part Two is negotiated with the supervisor.
Modules on the Health Data Science programme typically include:
Scientific Computing and Health Care
Health Data Modelling
Introductory Analysis of Linked Health Data
Machine Learning in Healthcare
Health Data Visualisation
Advanced Analysis of Linked Health Data
The College of Medicine offers the modules on the Health Data Science course as standalone opportunities for prospective students to undertake continued professional development (CPD) in the area of Health Data Science.
You can enroll on the individual modules for the Health Data Science programme as either an Associate Student (who will be required to complete the module(s) assessments) or as a Non-Associate Student (who can attend all teaching sessions but will not be required to complete any assessments).
For information and advice on applying for any of the continuing education opportunities, please contact the College directly at [email protected].
Postgraduate study has many benefits, including enhanced employability, career progression, intellectual reward and the opportunity to change direction with a conversion course.
From the moment you arrive in Swansea, specialist staff in Careers and Employability will help you plan and prepare for your future. They will help you identify and develop skills that will enable you to make the most of your postgraduate degree and enhance your career options. The services they offer will ensure that you have the best possible chance of success in the job market.
The student experience at Swansea University offers a wide range of opportunities for personal and professional development through involvement in many aspects of student life.
Co-curricular opportunities to develop employability skills include national and international work experience and study abroad programmes and volunteering, together with students' union and athletic union societies, social and leisure activities.
For the MSc Health Data Science course, we are in the process of identifying opportunities for our students to complete volunteering placements with a number of our collaborative partners.
This one year MSc Data Science degree prepares you to become a proficient data scientist, building core areas of expertise, from the ability to operate high-performance computing clusters and cloud-based infrastructures, to devising and applying sophisticated Big Data analytics techniques.
This MSc programme will train students to become proficient data scientists.
You will gain advanced knowledge in areas such as data mining, machine learning, and data visualization, including state of the art techniques, programming toolkit, and industrial and societal application scenarios.
This programme prepares you to become a proficient data scientist, developing your specialist knowledge in subjects that are crucial for mastering the vast and ever-so-complex information landscape that is characteristic to modern, digitally empowered organisations.
This is typically linked to a number of core areas of expertise, from the ability to operate high-performance computing clusters and cloud-based infrastructures, to the know-how that is required to devise and apply sophisticated Big Data analytics techniques, and the creativity involved in designing powerful visualizations.
In the first semester you start with a review of key topics in data science. The course will introduce the core theoretical and technology components required to design and use a data science application, using open-source tools and openly accessible data sets. You will also cover the most important machine learning techniques, which are at the core of any attempt to analyse and reason about data.
You will be exposed to more advanced topics in data mining in the second semester, including feature engineering, methods to manipulate text and multimedia data, topic modelling, social network analysis, and spectral analysis. A new module on data visualization will introduce the most common types of visualization techniques and state-of-the-art technology used to build graphic elements into data science applications to present analytics results.
Finally, during the summer the MSc project enables you will demonstrate your mastery of specialist techniques, relevant methods of enquiry, and your ability to design and deliver advanced application, systems and solutions to a tight deadline, including the production of a substantial dissertation.
Data scientists help organisations handle large amounts of data being produced thanks to digital technologies. Harvard Business Review described the role as 'The Sexiest Job of the 21st Century' due to the rare combination of skills that a trained data scientist possesses.
Data science has seen an unparalleled expansion as the data-driven economy grows. Increasingly organisations require skilled professionals who can handle large datasets and managers who can utilise the resulting analysis to make impactful decisions.
There is a range of potential jobs available; demand for big data staff is predicted to rise 92% over 5 years from Jan 2013. The programme provides an excellent opportunity for entry into data sciences or similar fields. Plus, big data positions offer a median salary of £55,000 – 24% higher than for IT staff in general (UK). There are also academic possibilities for doctoral study, as there are for entrepreneurial careers.
ECS runs a dedicated careers hub with is affiliated with more than 100 renowned companies such as IBM, Arm, Microsoft, Samsung, and Google. Visit our Careers Hub for more information.
Graduates from our MSc program can seek employment worldwide in:
Through an extensive blend of networks, mentors, societies and our on-campus startup incubator, we also support aspiring entrepreneurs looking to build their professional enterprise skills. Discover more about enterprise and entrepreneurship opportunities.
Data Science is a rapidly developing field of study within both academia and industry. Its interdisciplinary nature ensures its wide application domain. This MSc Data Science aims to prepare students for a successful career as a data scientist or business analyst working in any profession where large amounts of data is collected, hence there is a need for skills in data acquisition, information extraction, aggregation and representation, data analysis, knowledge extraction and explanation. These type of skills are typically in high demand in IT business, security and health sectors, intelligent transport, energy efficiency and the creative industries.
More generally data and analytics capabilities have developed rapidly in recent years. The volume of available data has grown exponentially, more sophisticated algorithms have been developed, and computational power and storage have steadily improved. Most companies, however, are not capturing the full potential value from data and analytics because they do not have the required expertise. Consequently, the MSc Data Science aims to address these challenges by providing a firm grounding in the core disciplines of data analytics and information processing, partnered with a broad appreciation of aspects of other disciplines where data science can form natural synergistic relationships.
Ulster University academics are actively involved in both research and teaching and this ensures that the developments accrued through research can feed into the teaching of students. A high percentage of staff are members of the Higher Education Academy, and all staff are expected to have a Postgraduate Certificate in University Teaching or equivalent. All Computing courses are subject to periodic Faculty Review and University Revalidation.
The key message from employability and work-related learning initiatives is that enhancing opportunities to develop work-related learning and employability enhances the learning of the subject being studied. We understand the importance of including real industrial and commercial contexts to our student's experience, so this MSc Data Science will pursue opportunities for industrially linked teaching material and student project work. In this regard, we will utilise our business and industry links to facilitate an industrially relevant student project. Such projects create valuable experiences for the student, and additionally, they can also help to build new and ongoing collaborations with departments and companies, with the potential to tap into funding streams designed for industry-academic research and development.
A recent statement from Ulster University’s Careers Office indicates that Data analysts are in high demand across all sectors, such as finance, consulting, manufacturing, pharmaceuticals, government and education. Data analysts can work in large companies such as the ‘big four’ consultancies or financial services firms, or consumer retail firms, small and medium sized businesses such as marketing agencies’ or the public sector.
The UCL programme in Data Science for Research in Health and Biomedicine covers computational and statistical methods as applied to problems in data-intensive medical research. Students learn techniques that are transforming medical research and creating exciting new commercial opportunities. Our recent graduates, many of whom begin paid internships while completing the MSc, have moved on to roles in industry and academia.
Students learn how to link and analyse large complex datasets. They design and carry out complex and innovative clinical research studies that take advantage of the increasing amount of available data about the health, behaviour and genetic make-up of small and large populations. The content is drawn from epidemiology, computer science, statistics and other fields, including genetics.
Students undertake modules to the value of 180 credits.
The programme consists of five core modules (75 credits), three optional modules (45 credits) and a dissertation/report (60 credits).
A Postgraduate Diploma (120 credits) is offered.
A Postgraduate Certificate (60 credits) is offered.
All students undertake an independent research project which culminates in a dissertation. Project Proposal 20% (2,000 words); Journal Article 80% (6,000 words).
Teaching and learning
The programme is delivered by clinicians, statisticians and computer scientists from UCL, including leading figures in data science. We use a combination of lectures, practical classes and seminars. A mixture of assessment methods is used including examinations and coursework.
Further information on modules and degree structure is available on the department website: Data Science for Research in Health and Biomedicine MSc
Students on this programme will be passionate about research and know that, in the 21st century, some of the most exciting, stimulating and productive research is carried out using large collections of data acquired in big collaborative endeavours or major public or private initiatives. We hope that graduates will build on that passion and, together with the experience gained on the programme, will go one to develop careers as entrepreneurs, scientists and managers, working in industry, academia and healthcare.
The programme is designed to meet a need, identified by the funders of health research and by a number of industrial organisations and healthcare agencies, for training in the creation, management and analysis of large datasets. This programme is practical, cross-disciplinary and closely linked to cutting-edge research and practice at UCL and UCL’s partner organisations. Data science is arguably the most rapidly growing field of employment at the moment and employers recruiting in health data science include government agencies, technology companies, consulting and research firms as well as scientific organisations. A number of employers are supporting the programme in different ways, including providing paid internships to selected students.
Data science is an exciting area with a dynamic job market, including in healthcare. Our graduates have gone on to work for a range of companies, including large research organisations and small start-ups, while others are working in health care or pursuing their interests in universities.
The lecturers on this programme are international experts in health data science and students will learn about cutting-edge research projects. The collaboration is part of the Farr Institute, a network of centres of excellence created to enhance the UK’s strength in data-intensive research. This MSc will draw on that collaboration, giving students access to the most advanced research in the field.
We work closely with a range of employing organisations to ensure that our graduates have the best possible preparation for a career in data science. This includes offering industry-sponsored dissertations for selected students.
The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.
Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.
This course is your opportunity to specialise in the development of web-based software systems that use databases. During your time with us, you will gain a critical awareness of the methodologies, tools and techniques used for the development of web-based computer systems and an advanced understanding of the techniques used for the development, evaluation and testing of databases.
The course also develops an awareness of the latest developments in the field of advanced databases, data mining and data warehousing. You will also gain substantial knowledge and skills in the deployment of SAS business intelligence software leading towards SAS data miner accreditation, and learn what the Semantic Web and Linked Data are, together with what these technologies enable.
This course covers a very comprehensive range of topics split in to four large modules worth 30 credits each plus the MSc Project. External speakers from blue-chip and local companies will give seminars to complement your learning, that will be real-world case studies related to the subjects you are studying in your modules. These are designed to improve the breadth of your learning and often lead to ideas that you can develop for your MSc Project.
Teaching on this course takes the form of lectures, individual and group class work, topical class discussions and critical case study evaluation.
You will gain hands-on lab experience of using and setting up databases and web-based systems. What’s more, tutorials will give you practice in solving the theoretical and design problems associated with these systems.
With this qualification, you’ll be equipped as web/database designer and programmer, data analytics and miner among other roles. Your experience will be in high demand across all industrial and commercial sectors.
Previous students have gone on to work with companies including British Airways, Google, Hewlett-Packard, Oracle and other IT firms.
Our links with industry include large companies (BT, Oracle, Microsoft) and local companies.
These companies engage with the University by giving guest seminars and often our students will work with them on their MSc Project.
Many of our graduates will go on to further study in our Computer Networks and Telecommunications Research Centre (CNTR)
The CNTR undertakes both pure and applied research in the general field of telecommunications and computer networking including computer networking technologies, wireless systems, networked multimedia applications, quality of service, mobile networking, intelligent buildings, context driven information systems and communication protocols. Much of this work is funded through research grants and supported by industry. In addition, members of the group are actively involved in a range of public engagement courses which aim to raise the awareness of these subjects for the general public and in schools.
Research themes in this Centre include:
At the Centre for Intelligent Systems and their Applications (CISA) we enable computer systems to reproduce or complement human abilities, work with people, and support collaboration between humans. We conduct world-leading research in the foundations of Artificial Intelligence (knowledge representation and reasoning, emergence of meaning, theory and ontology change, creativity, mathematical proof) and in intelligent collaborative systems (multiagent systems, social computation, scientific collaboration platforms, web semantics and linked data).
Our research methods are inspired by developing formal models of knowledge, reasoning, and interaction that can be used to understand and automate aspects of human intelligence, but are also understandable and usable to the human designers and users of AI systems.
To achieve this, we combine theoretical research into computational models, architectures, and algorithms with a strong element of applied research. This has led to a strong track record in using our methods to address real-world problems in healthcare, scientific collaboration, social computing, emergency systems, transportation, engineering, aerospace and others.
You'll find a wide range of research areas within CISA conducted in the four research groups the Institute currently hosts:
CISA includes one of the most innovative collaborations between research and business - our Artificial Intelligence Applications Institute (AIAI). Through its resources and the engagement of CISA staff and students in consultancy, training and joint projects, we help companies and government agencies to apply newly researched techniques.
You will carry out research work within a research group under the guidance of a supervisor. You may also attend taught courses that are relevant to your research topic, as prescribed by your supervisor. You will be expected to attend seminars and meetings of relevant research groups. Periodic reviews of progress are conducted to assist with research planning.
A programme of transferable skills courses facilitates broader professional development in a wide range of topics, from writing and presentation skills to entrepreneurship and career strategies.
The School of Informatics holds a Silver Athena SWAN award, in recognition of our commitment to advance the representation of women in science, mathematics, engineering and technology. The School is deploying a range of strategies to help female staff and students of all stages in their careers and we seek regular feedback from our research community on our performance.
The award-winning Informatics Forum is an international research facility for computing and related areas. It houses more than 400 research staff and students, providing office, meeting and social spaces.
It also contains two robotics labs, an instrumented multimedia room, eye-tracking and motion capture systems, and a full recording studio amongst other research facilities. Its spectacular atrium plays host to many events, from industry showcases and student hackathons to major research conferences.
Nearby teaching facilities include computer and teaching labs with more than 250 machines, 24-hour access to IT facilities for students, and comprehensive support provided by dedicated computing staff.
Among our entrepreneurial initiatives is Informatics Ventures, set up in 2008 to support globally ambitious software companies in Scotland and nurture a technology cluster to rival Boston, Pittsburgh, Kyoto and Silicon Valley.
While your research studies are a perfect route to a career in academia, your degree could also take you into the commercial world of applied AI and collaborative systems.
Software developers using AI technologies are among those who rely on the insights of our research. NASA and animation company Pixar are just two of the organisations that have recently employed our graduates.