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MSc Applied Data Science in Engineering

MSc Applied Data Science in Engineering

Different course options

Full time | Glasgow Caledonian University | 1 year | SEP-25

Study mode

Full time

Duration

1 year

Start date

SEP-25

Key information
DATA SOURCE : IDP Connect

Qualification type

MSc - Master of Science

Subject areas

Engineering Studies Data Science

Course type

Taught

Course Summary

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 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 with their engineering knowledge to ensure 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.

Graduate opportunities

The MSc Applied Data Science in Engineering offers graduates a highly focused skillset that is valuable to an extremely wide range of industry sectors currently going through the digital transformation process.

Across these industries you might focus on predictive analytics for asset performance, on solutions to increase uptime and decrease downtime, the use of instrumentation, big data, optimisation and engineering informed analytics via digital twins, on digital readiness or enhance decisions related to design, operations, or maintenance via data analytics.

Modules

Data Capture
Data Visualisation
Digital Twins
IOT Framework Informatics
Predictive Maintenance
Professional Practice
Software Development for Data Science
System Health Management

Tuition fees

UK fees
Course fees for UK students

Contact University and ask about this fee

International fees
Course fees for EU and international students

For this course (per year)

£18,800

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.