MSc Applied Data Science in Engineering

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.

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.

Due to significant demand, applications for this course are now closed to international applicants for January 2022. Please consider our following intake in September 2022.

You 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.

Module list

Trimester A: Software Development for Data Science, Data Capture, System Health Management, Predictive Maintenance

Trimester B: IOT Framework Informatics, Data Visualisation, Digital Twins, Professional Practice
 

Study Options

  • 2022/23

Award

Mode of study

Duration

Start date

Location

MSc
Distance Learning
2 - 5 Years
Jan 2023
GCU Glasgow
MSc
Full Time
16 Months
Jan 2023
GCU Glasgow
MSc
Distance Learning
2 - 5 Years
Sep 2022
GCU Glasgow
MSc
Full Time
1 Year
Sep 2022
GCU Glasgow
Award Mode of study Duration Start date Location  
MSc Distance Learning 2 - 5 Years Jan 2023 GCU Glasgow Enquire Apply
MSc Full Time 16 Months Jan 2023 GCU Glasgow Enquire Apply
MSc Distance Learning 2 - 5 Years Sep 2022 GCU Glasgow Enquire Apply
MSc Full Time 1 Year Sep 2022 GCU Glasgow Enquire Apply

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

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

Please note: if you are from a majority English speaking country, you may not be required to provide further proof of your English Language proficiency.

Additional information

Other academic and vocational qualifications

Each application to GCU is considered on an individual basis. 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 scheme.

The tuition fees you pay are mostly determined by your fee status. What is my student fee status?

Annual full-time tuition fees

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

Part-time and distance-learning tuition fees

View our part-time and distance-learning fee schedules

Fees are subject to change and published here for guidance only.

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.

View additional costs

Scholarships

We provide high-quality education for a fair price; as the University for the Common Good, we are committed to offering accessible higher education for talented students by keeping our tuition fees low and providing a generous scholarship package of £2 million per year.

View our postgraduate scholarships.

This course features guest lectures and talks from industrialists and digital transformation practitioners.

We aim to offer projects based on current and relevant industrial challenges in the data science and data enabled solutions space.

We will encourage and support students’ applications for membership with the UKPEIS (for example, the Institute of Measurement and Control, Institute of Engineering and Technology, and so on)

The course team will arrange site visits to local engineering organisations acting as pioneers in deployment and utilisation of data-enabled solutions and digital twining.
The course team will arrange site visits to local engineering organisations acting as pioneers in deployment and utilisation of data-enabled solutions and digital twining.

Accreditation will be sought from at least one of the UK PEIs registered with the Engineering Council.

This course features guest lectures and talks from industrialists and digital transformation practitioners.
This course has been designed with industry input.

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.