MSc Applied Data Science in Engineering

at Glasgow Caledonian University

Module

Trimester A: IOT Framework Informatics; Software Development for Data Science; Data Capture; System Health Management

Trimester B: Data Visualisation; Digital Twins; Professional Practice; Predictive Maintenance

All entry requirements listed here should be used as a guide and represent the minimum required to be considered for entry. Applicants who are made a conditional offer of a place may be asked to achieve more than is stated.

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.

English language requirements
Academic IELTS score of 6.0 (or equivalent) with no element below 5.5.

Annual full-time tuition fees

Home (Scottish) and RUK: £7,000
International: £14,500

Additional costs
As a student at the University, there are additional fees and costs which may or may not apply to you, but that you should be aware of.

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.

When you graduate, you will be a competitive candidate for roles such as data analyst, data scientist, and data-enabled solutions designer for predictive capabilities targeted at complex assets.

You might also want to pursue a career as a digital change leader for an engineering organisation bridging the knowledge gap between subject matter experts and domain knowledge, data scientists, data engineers and architects, IT/OT specialists and business owners.

You can also use the course as a foundational knowledge base for PhD studies in the Applied Data Science or Data-Enabled Industrial Engineering fields.