For those interested in training while they work, our flexible online learning programmes are designed with working professionals in mind. Upskill with a short free Massive Open Online Course (MOOC), take a single course from our selection of online postgraduate courses within the Data Science, Technology and Innovation programme, or study to certificate, diploma or masters (MSc) level completely online.
Online Learning Postgraduate Programmes
Data Science, Technology and Innovation
The Data Science, Technology and Innovation (DSTI) postgraduate programme is a University-wide online learning programme hosted by the College of Science and Engineering with operations based in the Bayes Centre. This flexible, modular programme is designed to fully equip tomorrow's data professionals, offering different entry points into the world of data science – with courses available across the sciences, medicine, arts and humanities. Students are able to study a single or multiple courses via Postgraduate Professional Development (PPD) or study at a PG Certificate, PG Diploma or Masters (MSc) level, with an option to specialise in Medical Informatics at MSc level.
Demand is growing for high value data specialists with high level skills to turn stockpiles of information into knowledge for better decision making. Our modular online framework means that working professionals from diverse backgrounds can pursue professional development in a variety of forms while continuing to fulfil professional commitments. This programme aims 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.
Further information is available on the programme website.
Massive Open Online Courses (MOOCs)
Data Science in Stratified Healthcare and Precision Medicine
An increasing volume of data is becoming available in biomedicine and healthcare, from genomic data to electronic patient records and data collected by wearable devices. Recent advances in data science are transforming the life sciences, leading to precision medicine and stratified healthcare.
This course covers some of the different types of data and computational methods involved in stratified healthcare and precision medicine. Learners have a hands-on experience of working with such data and learn from leaders in the field about successful case studies.
Topics include: sequence processing, image analysis, network modelling, probabilistic modelling, machine learning, natural language processing, process modelling and graph data.
Statistics: Unlocking the World of Data
At a time where understanding data is becoming more and more important, this course will introduce learners to the fundamentals of statistics, and provide them with a toolkit to better understand the data and statistics we encounter in everyday life.
The course looks at what data is, how we present it, and how statistics is used to investigate many challenging issues and scientific advances, from global warming to modern-day slavery, or from the decline of biodiversity to advances in pharmaceutical research. Through a combination of videos, examples, interactive apps, discussions and quizzes, learners are taken on a journey into the heart of statistics.
Today’s supercomputers are the most powerful calculating machines ever invented, capable of performing more than a thousand million million calculations every second. This gives scientists and engineers a powerful new tool to study the natural world – computer simulation.
Using supercomputers, we can now conduct virtual experiments that are impossible in the real world – from looking deep inside individual atoms, to studying the future climate of the earth and following the evolution of the entire universe from the Big Bang.
This free online course introduces what supercomputers are, how they are used and how we can exploit their full computational potential to make scientific breakthroughs.
To find out more about MOOCs at the University of Edinburgh, take a look at their web pages.