Embark on a career in a rapidly growing field and become a data scientist with an engineering background within a very lucrative field. GCU’s MSc in Applied Data Science in Engineering will ensure you will become a competent specialist in Engineering Informed Data Science (EIDS) tools and technologies (or solutions) for high-value, highly complex assets. As part of this course, you will study a ground-breaking curriculum linked to industry digital engineering needs. You will learn to analyse complex systems and engineering assets, to deploy instrumentation as part of Industrial Internet of Things (IIoT) architectures, to store, manipulate and analyse big data effectively by implementing data visualisation techniques and producing digital twins capable of transforming data into actionable insights supporting informed engineering/business decisions. With both full-time and distance learning study available, the course was designed with input from industry for industry, and it was specifically constructed with a career development focus, so you will gain valuable skills you can immediately put to work in different industry sectors. Apply your engineering domain knowledge to develop high-quality data science tools and solutions for physical systems. Data scientists with an engineering background apply their engineering knowledge to ensure a higher quality of data. Explore industry-standard commercial off-the-shelf solutions for system-level analysis and design of IIoT platforms and augmented-reality-enabled digital twins. Develop and apply predictive analytics to support data-informed engineering and business decisions. Learn how to develop data visualisation dashboards to maximize the level of engineering insights related to asset performance, health management, operations, maintainability and through-life engineering support solutions. The course requirements were captured via in-depth interviews with representatives of Scottish engineering firms (part of global engineering organisations) and the taught modules were designed to fulfil a real need in terms of digital engineering skills. Input from engineering institutes and governmental bodies was also captured to ensure the relevance of the curriculum in the context of digitalisation of assets’ design, manufacturing, operations, and through-life engineering support of complex systems. Our goal is to deliver competent candidates ready to deliver value in the exciting journey of digital transformation. Minimum Entry Requirements UK honours degree 2:2 (or equivalent) in engineering (for example. mechanical, telecommunications and petroleum), physical sciences, and computer/IT engineering. We also welcome applicants with industry qualifications/experience within the GCU Recognition of Prior Learning (RPL) Policy.
ou will be studying eight taught modules, delivered in two trimesters over 12 weeks. At the end of the taught course, you will be attached to a Project Module and over three months, you will be working on an individual research project meant to apply, in a systematic manner, the fundamental knowledge and skills of the digital transformation by harnessing and exploiting data from physical assets. We strive to offer projects derived from industrial challenges and supported by our industrial partners or your employer.
Assessment is used to demonstrate the achievement of learning outcomes. The methods of assessment include class tests, coursework assignments, practical tests, and technical reports. Practical implementation and evaluation form a significant part of the assessment for the taught modules and for the work of the MSc dissertation.
UK honours degree 2:2 (or equivalent) in engineering (for example. mechanical, telecommunications and petroleum), physical sciences, and computer/IT engineering. We also welcome applicants with industry qualifications/experience within the GCU Recognition of Prior Learning (RPL) Policy.
Fees and funding