MSc Applied Data Science in Engineering

1 Years On Campus Masters Program

Glasgow Caledonian University

Program Overview

The MSc in Applied Data Science in Engineering at GCU Glasgow blends engineering knowledge with data science, analytics, and digital-engineering techniques. It suits graduates from engineering, physical sciences or related fields who want to work as data-enabled engineers, predictive-maintenance specialists or industrial data scientists.

Curriculum Structure (Full-time, 12–16 Months)

Year of Study

Students begin with Software Development for Data Science and Data Capture, learning programming, data-collection methods and how to ingest sensor and operational data from engineering systems. They continue with System Health Management and Predictive Maintenance, where they study asset monitoring, performance modelling and the use of data-driven techniques to forecast failures and optimise maintenance strategies. Later modules such as Digital Twins and Data Visualisation teach students to create digital replicas of engineering systems and transform large datasets into meaningful insights for operational decision-making. The programme concludes with Professional Practice and an MSc Project, where students apply engineering analytics, IoT data, modelling and visualisation to deliver a real-world data-driven solution.

Focus areas: “Engineering data science, IoT data capture, predictive maintenance, digital twins, asset analytics, data visualisation, applied engineering project”

Learning outcomes: “Develop software and pipelines for engineering data; analyse system health; build predictive-maintenance models; design digital-twin systems; visualise engineering data; complete a comprehensive engineering-data project.”

Professional alignment (accreditation): Designed to support progression toward Chartered Engineer (CEng) status for students entering with accredited engineering degrees.

Reputation (employability rankings): Highly aligned with industry needs, the programme prepares graduates for roles in manufacturing, energy, utilities, infrastructure and advanced industrial engineering, where data-driven decision-making is increasingly essential.

Experiential Learning (Research, Projects, Internships etc.)

The MSc Applied Data Science in Engineering at Glasgow Caledonian University (GCU) focuses on developing practical data analysis and machine learning skills for engineering applications. Students work with real-world engineering datasets using specialised software and high-performance computing to solve problems in areas like predictive maintenance, smart systems, and process optimisation.

Key experiential components:

Software & Tools: Data analysis and modelling using Python (with libraries like Pandas, Scikit-learn, TensorFlow/PyTorch), R, MATLAB, and engineering simulation software for data integration and analysis.
Computing Facilities: Access to GCU's high-performance computing resources and engineering computing labs, equipped for processing large sensor datasets and running complex computational models.
Group Projects: A core collaborative engineering data project, where multidisciplinary teams tackle a data-driven challenge from sectors such as energy, manufacturing, or civil engineering, following the full analytics lifecycle.
Industry & Research Application: The curriculum is directly linked to GCU's Engineering Research Centres and industry partnerships, ensuring projects and dissertations address current engineering problems requiring data science solutions.

Progression & Future Opportunities

Graduates of Glasgow Caledonian University's MSc Applied Data Science in Engineering become data scientists, data analysts, predictive analytics specialists, and digital twin engineers in engineering, manufacturing, energy, and digital transformation sectors:

Careers Service provides CV workshops, interview coaching, employer events, and placement opportunities.
93% in work/further study at 15 months; competitive salaries (£30k+ UK start) with strong prospects (81% on track).
Industry-designed curriculum with Data Lab collaborations for real-world EIDS tools and IIoT projects.
Skills support certifications for senior data leadership and lifelong engineering careers.
Outcomes in predictive analytics, asset optimization, or research across high-value industries.

Further Academic Progression: Graduates can pursue PhD in data science or engineering at GCU/elsewhere, extending MSc dissertation on AI/ML, big data, or digital twins.

Additional Information & 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).

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

TOEFL: Standard 92 overall (minimum 20 in all elements)

INDIAN (CBSE/ICSE/ISC/STATE BOARD): Bachelor's degree from Indian Institutes of National Importance or recognised universities with First Class Honours or around 70%+ overall.

AMERICAN (GPA/AP): 4-year Bachelor's degree with minimum GPA ~3.4/4.0 or higher, in a related field.

China- Bachelor's degree at grade 65% from a tier one university and 70% from a tier two or tier three university.

B grade in GCSE Mathematics or equivalent; or

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