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Data Science, Technology & Innovation MSc/PgDip/PgCert/PgProfDev


Course Description

Programme description

Demand is growing for high value data specialists across the sciences, medicine, arts and humanities. The aim of this unique, modular, online distance learning programme is to enhance existing career paths with an additional dimension in data science.

The programme is designed to fully equip tomorrow’s data professionals, offering different entry points into the world of data science – across the sciences, medicine, arts and humanities.

Students will develop a strong knowledge foundation of specific disciplines as well as direction in technology, concentrating on the practical application of data research in the real world.

You can study to an MSc, Postgraduate Diploma, Postgraduate Certificate or Postgraduate Professional Development level.

Online learning

Our online learning technology is fully interactive, award-winning and enables you to communicate with our highly qualified teaching staff from the comfort of your own home or workplace.

Our online students not only have access to the University of Edinburgh’s excellent resources, but also become part of a supportive online community, bringing together students and tutors from around the world.

Programme structure

You can study to an MSc, Postgraduate Diploma, Postgraduate Certificate or Postgraduate Professional Development level.

For the MSc programme, students must successfully complete a total of 180 credits: Practical Introduction to Data Science (20 credits), the Dissertation Project (60 credits) plus 100 credits from the list of courses below.

For the MSc with specialism in Medical Informatics, students must successfully complete a total of 180 credits: Medical Informatics (10 credits), Research and Evaluation in eHealth (10 credits), the Dissertation Project (60 credits) plus 100 credits from the list of courses below. Students wishing to study the MSc with specialism in Medical Informatics should apply for the standard MSc in Data Science, Technology and Innovation and contact the Programme Administrator to discuss the specialism.

For the Postgraduate Diploma (PG Dip), students must successfully complete a total of 120 credits: Practical Introduction to Data Science (20 credits) plus 100 credits from the list of courses below.

For the Postgraduate Certificate (PgCert), students must successfully complete a total of 60 credits: Practical Introduction to Data Science (20 credits) plus 40 credits from the list of courses below.

For the Postgraduate Professional Development (PPD), students may take a maximum of 50 credits from the list of courses below. These credits will be recognised in their own right for postgraduate level credits or may be put towards gaining a higher award such as a PgCert.

Option courses

Some option courses may be compulsory for a specific programme; please refer to the information above.

Advanced Vision (10 credits)
Engaging with Digital Research (10 credits)
Ethics and Governance of eHealth (10 credits)
Introduction to Clinical Trials (10 credits)
Introduction to Health Informatics 1 (10 credits)
Introduction to Health Informatics 2 (10 credits)
Introduction to Vision and Robotics (10 credits)
Machine Learning (10 credits)
Managing Digital Influence (10 credits)
Medical Informatics (10 credits)
Neuroimaging: Common Image Processing Techniques 1 (20 credits)
Neuroimaging: Common Image Processing Techniques 2 (10 credits)
Practical Introduction to Data Science (20 credits)
Practical Introduction to High Performance Computing (20 credits)
Public Health Informatics (10 credits)
Research and Evaluation in eHealth (10 credits) (restricted to the MSc and MSc with Medical Informatics programmes)
Social Shaping of Digital Research (10 credits)
Technologies of Civic Participation (10 credits)
Telemedicine and Telehealth (10 credits)
The Use and Evolution of Digital Data Analysis and Collection Tools (10 credits)
Understanding Data Visualisation (10 credits)
User Centred Design in eHealth (10 credits)
Dissertation project – all Masters

(We recommend you take Introduction to Vision and Robotics before or simultaneously taking Advanced Vision, or have some previous experience with image processing.)

Learning outcomes

The modular course structure offers broad engagement at different career stages. Individual courses provide an understanding of modern data-intensive approaches while the programme provides the knowledge base to develop a career that majors in data science in an applied domain.

Career opportunities

This programme is intended for professionals wishing to develop an awareness of applications and implications of data intensive systems. Our aim is to enhance existing career paths with an additional dimension in data science, through new technological skills and/or better ability to engage with data in target domains of application.

Visit the Data Science, Technology & Innovation MSc/PgDip/PgCert/PgProfDev page on the University of Edinburgh website for more details!

Entry Requirements

An undergraduate degree, with an excellent or very good classification (equivalent to first or upper second class honours in the UK). Applicants who fall below these entry requirements, but have relevant work experience, may be considered on a case-by-case basis at the discretion of the programme director.

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