Overview

Engineering is becoming smarter, faster and more connected, and data is driving that transformation. Data is now central to how systems are designed, monitored and improved. Organisations across the world now rely on professionals who can turn complex engineering data into practical solutions that improve performance, efficiency and sustainability.

Our MSc course prepares you to lead that shift.

You’ll apply advanced data science techniques directly to real engineering applications, developing the skills to solve challenges across industries such as:

energy
transport
manufacturing
infrastructure.

From predictive analytics and data visualisation to Industrial Internet of Things (IIoT) networks and digital twins, you’ll work with the technologies shaping the future of engineering. You’ll learn how to improve asset maintenance and performance as well as create data-driven tools and visual dashboards that support smarter decision-making across industries.

Designed with Scottish engineering firms, professional bodies and international industry partners, this practical MSc has direct industry relevance. You’ll explore how data is captured, analysed and applied within real engineering systems and assets. Alongside this, you’ll gain hands-on experience with industry-relevant tools and software, helping you develop the technical and analytical skills employers are actively looking for

Taught by internationally experienced academics, teaching is closely connected to industry practice. Guest speakers, networking opportunities and site visits will expose you to emerging technologies and real-world engineering challenges, helping you understand how organisations are using data science to transform operations and decision making.

Whether you want to progress your engineering career, move into specialist data-focused roles, or support digital transformation projects, this course gives you the expertise to stand out in a rapidly growing global field.

Choose how you study

Study full-time on campus at our Glasgow campus, or choose flexible online distance learning and study from anywhere in the world.

You’ll study the same course content and modules whichever mode you choose, giving you the freedom to learn in a way that suits your goals and circumstances.

Online distance learning is delivered part-time, making it easier to balance your studies with work and other commitments.

Why study Applied Data Science in Engineering at GCU?

Study a practical engineering-focussed MSc. You’ll apply data science directly to real engineering systems, not just theoretical data analysis.
Benefit from a course designed in collaboration with Scottish engineering firms and international partners to reflect current and emerging industry needs.
Gain hands-on experience with Industrial Internet of Things (IIoT), digital twins and predictive analytics, apply new skills to real-world assets and infrastructure.
Graduate with a globally relevant degree that combines Scotland’s engineering heritage with modern digital innovation to prepare you for an international career.
Available to study full-time on campus, or online part-time. Whether you’re a working professional or recent graduate, you’ll be able to gain advanced data and digital skills.

The Institution of Engineering and Technology

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The Institute of Measurement and Control

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What you will study

Core modules

Software Development for Data Science

System Health Management

Data Capture

IoT Framework Informatics

Professional Practice (Engineering)

Data Visualisation

Digital Twins

Predictive Maintenance

Research project

Project

Please note that full module information is in the process of being finalised for publication and will be made available here shortly.

In the meantime, you can view and download the Programme Specification at the bottom of this page. This document provides an overview of the course structure, including indicative modules, learning content and further key information. The modules listed on the Programme Specification are for the current academic year and are intended to give you an idea of what you may be able to study on this course.

Careers

Graduates of this MSc develop a combination of engineering knowledge and data science expertise that is increasingly valued across industries undergoing digital transformation. These skills can support career opportunities in a range of sectors.

Manufacturing and industrial automation

Use data and predictive analytics to optimise production and improve equipment reliability.

Typical roles include:
industrial data engineer
predictive maintenance engineer
reliability or prognostics engineer
IIoT systems engineer
digital twin developer.

Energy and utilities

Help modernise energy systems by improving asset performance, reducing downtime, and supporting sustainability goals.

Careers may include:

asset performance analyst
data scientist (energy systems)
optimisation engineer
digital transformation specialist.

Transport, aerospace and infrastructure

Apply data science to monitor and enhance large-scale, safety-critical systems.

Graduates often pursue roles such as:

data analyst (transport or infrastructure assets)
machine learning engineer (engineering focus)
digital twin engineer.

Technology, consulting and research

Support organisations on their journey toward data-driven innovation or continue into advanced research.

Potential roles include:

data scientist (engineering applications)
data solutions architect
digital transformation leader or consultant
applied researcher in data-enabled engineering.

Course information

Award	Mode of study	Duration	Start date	Location
MSc	Full time	16 Months	Jan 27	Glasgow	Enquire Apply
Award	Mode of study	Duration	Start date	Location
MSc	Distance learning	2 - 5 Years	Jan 27	Distance learning	Enquire Apply
Award	Mode of study	Duration	Start date	Location
MSc	Distance learning	2 - 5 Years	Sep 27	Distance learning	Enquire
Award	Mode of study	Duration	Start date	Location
MSc	Full time	1 year	Sep 27	Glasgow	Enquire
Award	Mode of study	Duration	Start date	Location
MSc	Full time	16 Months	Jan 28	Glasgow	Enquire
Award	Mode of study	Duration	Start date	Location
MSc	Distance learning	2 - 5 Years	Jan 28	Distance learning	Enquire

Please note that admission to GCU is considered on a competitive basis, and the academic entry requirements stated here may be the minimum required to be eligible for an offer.

International students can get information on entry requirements specific to their country in our International Study section.

Entry requirements

UK honours degree 2:2 (or equivalent) in engineering or physical sciences (for example, mechanical, telecommunications and petroleum, physics, mathematics, electrical engineering, chemical 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

Additional information for international applicants

Entry requirements by country
Application deadlines
English language tests
Knowledge of English - Applicants from a majority English speaking country may not be required to provide further proof of your English language proficiency. Information about this requirement is available on the Student Visa section of the gov.uk website.

Additional information for all applicants

Relevant experience (Recognition of Prior Learning)

GCU's flexible entry policies exist to allow relevant work experience and prior learning to be considered towards standard entry or advanced entry into a course.

If you do not have the typical academic entry qualifications, but can demonstrate relevant work experience and/or credits from recognised professional bodies, you may be eligible to enter this course via the University's Recognition of Prior Learning (RPL) scheme.

Teaching methods

The curriculum combines lectures, tutorials and practical learning activities to help you build both theoretical knowledge and technical skills. Through hands-on projects, you'll work with engineering data across the full lifecycle, from data collection and network communications to analysis, modelling and visualisation, using industry-relevant tools and techniques to generate insights for engineering applications.

Exchange and study abroad

Assessment throughout the course is carefully designed to demonstrate achievement of the learning outcomes and reflect applied skills. A variety of methods are used, including:

class tests
coursework
practical assessments
technical reports
oral presentations.

These approaches ensure you are being evaluated on your theoretical understanding and your ability to apply concepts to real engineering and data science problems.

A strong emphasis is placed on practical implementation and evaluation. Each taught module incorporates applied elements that mirror real industrial scenarios.

Your dissertation gives you the opportunity to plan and deliver a substantial research or industry-based project.

Industry input

This course has been designed in close consultation with leading engineering firms, professional bodies, and technology specialists. This ensures its content is always aligned with the latest industrial developments. Modules are continually reviewed and updated to reflect advances in industry.

By embedding industry input into the course, you will graduate with knowledge and skills that are directly relevant to the challenges faced by employers across engineering, manufacturing, energy and infrastructure.

Guest lectures and networking events

You will benefit from regular guest lectures delivered by practising engineers, digital transformation leaders and specialists in data-enabled systems. These sessions expose you to current trends and emerging technologies. They offer practical insights into how data science is being used to optimise complex industrial processes.

Many guest speakers are GCU graduates who have gone on to successful careers in engineering analytics, asset management and technology consultancy. They can offer a realistic and honest perspective on future career pathways.

Networking events and alumni talks provide valuable opportunities to engage with potential employers and professional mentors.

Site visits and industry engagement

To strengthen the connection between learning and practice, the course involves site visits. We select pioneering organisations that are shaping the future of engineering through data-driven innovation, predictive maintenance and digital twin technologies. These local site visits enable you to see how concepts taught in the classroom can be applied to solve real industrial challenges and interact with experts and professionals in this field (the cost of the industrial visits will be covered by GCU).

Accreditation

The MSc Applied Data Science in Engineering is accredited by the Institute of Measurement and Control (InstMC) and Institution of Engineering and Technology (IET) on behalf of the Engineering Council as meeting the requirements for Further Learning for registration as a Chartered Engineer.

Candidates must hold a CEng accredited BEng/BSc (Hons) undergraduate first degree to fully meet the CEng registration educational requirements.

Accreditation enhances the academic credibility of the programme and supports progression towards Chartered Engineer (CEng) status. We encourage you to apply for membership with the InstMC and IET during your studies. This demonstrates professional commitment and connects you with a global network of engineers and technologists.

Visit the Institute of Measurement and Control website

Visit the Engineering Council website

Visit the IET website

Download the programme specification

In addition to the core modules that all students must undertake, some courses offer students a choice from a selection of optional modules to shape the focus of their degree. Optional modules where available, form part of the overall credit structure of the course.

Download the programme specification for a detailed breakdown of the course structure, including modules, what you'll learn and other useful information.

Terms and Conditions

Details of the University’s terms and conditions are available here.