We are passionate about sharing with our students the vital role they each have now and as future professionals in promoting a sustainable future for all. We believe that sustainability is not the domain of one discipline or profession. It is the responsibility of all disciplines, professions, organisations and individuals.
That is why on each of our courses within the Belfast School of Architecture and the Built Environment you will learn about the UN Sustainable Development Goals and the contribution you can make now, and as a graduate in the Built Environment.
Read the course details below to find out more.
Digital Construction at Ulster is an industry-linked highly creative course, specialising in BIM, Digital Technologies and Data Analytics.
This MSc in Digital Construction is the perfect course if you are passionate about discovering how the advanced digital technologies and BIM have transformed AEC industry, and understanding how Data Analytics can improve the management of construction projects.
This stimulating course will develop your research and analytical skills, and provide you with advanced learning in digital transformation modelling and practices. You will explore Digital Construction Technologies, Management, Strategies; Building Information Modelling; and the fundamentals of Data Analytics.
Unlike other similar courses in the field, the key features of our course can be described by: the inclusion of Data Analytics modules; the strong link with industry to help you engage with project environments in order to provide digital solutions for real industry problems; and you will be also taught by published academics who will enhance your learning experience with research-led teaching. This MSc course provides an excellent foundation for further study, as well as a gateway to a wide range of employment opportunities.
We know that choosing to study at university is a big decision, and you may not always be able to find the information you need online.
Please contact Ulster University with any queries or questions you might have about:
For any queries regarding getting help with your application, please select Admissions in the drop down below.
For queries related to course content, including modules and placements, please select Course specific information.
We look forward to hearing from you.
In this section
Digital platforms enable greater connectivity of construction processes to accomplish sustainable projects by simulating, predicting, optimising, managing and monitoring various UN Sustainable Development Goals within the AEC industries especially with the vision towards connecting the real and digital worlds.
Our MSc course is designed to introduce you to the essential areas of digital construction concepts, technologies, BIM and Data Analytics that will help you develop your career and deepening, and widening your understanding of the digital environment that the industry operates nowadays.
This Ulster MSc is a joint course between the Belfast school of Architecture & Built Environment and the school of Computing, Engineering & Intelligent Systems designed to meet the needs of experienced or aspiring professionals who wish to expand their knowledge and skills to equip them for the challenges facing the AEC industry.
By joining this Ulster MSc in Digital Construction you will be joining a group of diverse professionals who come from all over the world to study with us, and as such you will expose yourself to different cultures and develop an international network of professionals.
At Ulster we put a particular emphasis on equipping students with the theoretical and practical knowledge and skills to put into practice the learning from the MSc. You will engage with project environment in order to provide digital solutions for real industry problems. We also invite leading industry people to give guest lectures to MSc students.
The course is based on a modular structure with three modules in each semester. Attendance is a mix of class-based, computer-lab, and group work. For full time students, nine hours of staff contact time per week and 36 hours self-directed study per week (all on average).
The modules in Semester 1 will enable you to obtain fundamental knowledge on the subjects of Digital Construction, BIM and Data Analytics. In Semester 2, you will obtain in-depth knowledge on Data Analytics and develop advanced skills in applying your knowledge on Digital Construction in a project-based environment. In Semester 3, you will work on a specific research topic to advance your research skills and explore new areas in the field of Digital Construction.
Over the duration of the course you will develop your knowledge of the technical, theoretical and practical contexts which have led the evolution of a dynamic and innovative digital construction industry.
We aim to provide a learning and teaching environment where innovation and discovery are central to your education and in which you can take pride that you are an Ulster graduate. Many of the modules are research informed, where you can gain hands-on experience in contemporary research laboratories alongside current research projects. There will be an emphasis on challenge and collaboration, exploring new concepts and real-world construction problems. This will help you to be creative in solution thinking, both individually and cooperatively, so you can develop your own and other’s employable skills.
Teaching is delivered primarily through lectures, computer-labs, group work, and presentations from industry professionals. Project-driven and enquiry-based approaches to learning are central to the course, which includes practical challenges, real-world case studies, team-driven assessments/presentations and industry projects. The use of digital technologies will enrich our learning and teaching environment. Learning and teaching on the course makes use of a suite of Digital Learning tools to create a learning community within Virtual Learning Environment (Blackboard Learn) and Common Data Environments for construction industry. These will support all course modes of delivery (including Distance Learning) and host module areas complete with learning materials of varied media and interactive tools for student engagement, communication and collaboration, online assessment and feedback and content delivery offering you the flexibility to study at your own pace, any place and time.
A range of assessment techniques will be used to enable you to demonstrate your knowledge and understanding of the course concepts and content but also to enable interaction with your peers and the course team. These will include Blogs, chats, on-line forums, Essays and reports, presentations and simulations, and Peer review. The course is assessed in a number of ways to allow us to provide you with valuable feedback on your progress. These mainly include coursework and class tests. All assessments will have clearly identified marking criteria and you will be provided with clear briefs.
The course is designed to cover topics that address key industry challenges within the themes of Digital Construction from the view of addressing global as well as local challenges, thereby ensures students are global citizens. You are expected to apply the obtained Data Science knowledge in the field of Digital Construction; for example, to develop solutions for challenges of: Lean Construction to reduce waste, Generative Design to achieve Net-Zero emission, product innovation, whole life asset performance, evaluating new relevant technologies (e.g. off-site manufacturing), and achieving energy efficient construction. Example of design challenges within Computing modules include: big data processing, data visualisation dashboards and dealing with bias in machine learning applications. Mini-project-based or design-challenge assessment methods are also used throughout the course to also embed a sustainability focus where students develop solutions for low-impact computing and data innovation to ensure the sustainable or viability of business critical systems.
The content for each course is summarised on the relevant course page, along with an overview of the modules that make up the course.
Each course is approved by the University and meets the expectations of:
As part of your course induction, you will be provided with details of the organisation and management of the course, including attendance and assessment requirements - usually in the form of a timetable. For full-time courses, the precise timetable for each semester is not confirmed until near the start date and may be subject to change in the early weeks as all courses settle into their planned patterns. For part-time courses which require attendance on particular days and times, an expectation of the days of attendance will often be included in the letter of offer. A course handbook is also made available.
Courses comprise modules for which the notional effort involved is indicated by its credit rating. Each credit point represents 10 hours of student effort. Undergraduate courses typically contain 10- or 20-credit modules and postgraduate course typically 15- or 30-credit modules.
The normal study load expectation for an undergraduate full-time course of study in the standard academic year is 120 credit points. This amounts to around 36-42 hours of expected teaching and learning per week, inclusive of attendance requirements for lectures, seminars, tutorials, practical work, fieldwork or other scheduled classes, private study, and assessment. Part-time study load is the same as full-time pro-rata, with each credit point representing 10 hours of student effort.
Postgraduate Masters courses typically comprise 180 credits, taken in three semesters when studied full-time. A Postgraduate Certificate (PGCert) comprises 60 credits and can usually be completed on a part-time basis in one year. A 120-credit Postgraduate Diploma (PGDip) can usually be completed on a part-time basis in two years.
Class contact times vary by course and type of module. Typically, for a module predominantly delivered through lectures you can expect at least 3 contact hours per week (lectures/seminars/tutorials). Laboratory classes often require a greater intensity of attendance in blocks. Some modules may combine lecture and laboratory. The precise model will depend on the course you apply for and may be subject to change from year to year for quality or enhancement reasons. Prospective students will be consulted about any significant changes.
Assessment methods vary and are defined explicitly in each module. Assessment can be via one method or a combination e.g. examination and coursework . Assessment is designed to assess your achievement of the module’s stated learning outcomes. You can expect to receive timely feedback on all coursework assessment. The precise assessment will depend on the module and may be subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.
Coursework can take many forms, for example: essay, report, seminar paper, test, presentation, dissertation, design, artefacts, portfolio, journal, group work. The precise form and combination of assessment will depend on the course you apply for and the module. Details will be made available in advance through induction, the course handbook, the module specification and the assessment timetable. The details are subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.
Normally, a module will have four learning outcomes, and no more than two items of assessment. An item of assessment can comprise more than one task. The notional workload and the equivalence across types of assessment is standardised.
The class of Honours awarded in Bachelor’s degrees is usually determined by calculation of an aggregate mark based on performance across the modules at Levels 5 and 6 (which correspond to the second and third year of full-time attendance).
Level 6 modules contribute 70% of the aggregate mark and Level 5 contributes 30% to the calculation of the class of the award. Classification of integrated Masters degrees with Honours include a Level 7 component. The calculation in this case is: 50% Level 7, 30% Level 6, 20% Level 5. At least half the Level 5 modules must be studied at the University for Level 5 to be included in the calculation of the class.
All other qualifications have an overall grade determined by results in modules from the final level of study. In Masters degrees of more than 200 credit points the final 120 points usually determine the overall grading.
Figures correct for academic year 2019-2020.
Teaching is informed by academic research at Belfast School of Architecture and Built Environment. The Built Environment (Construction Eng. & Mgmt) programme at Ulster ranked 4th out of 54 programmes in the UK Building League Table in the last two Complete University Guide (2021 & 2022). The Built Environment programme team members at Ulster University have a strong track record in the area of BIM and Digital Construction research, publishing widely in the area since 2013, and supervising Knowledge Transfer Partnerships with industry on Digital Twins. The Computing, Engineering, Intelligent Systems team are active researchers within the Cognitive Analytics Research Lab (CARL). The contribution of Ulster team to Digital Construction and BIM research in Ireland between the period 2016 – 2020 has been acknowledged in a recent publication at CITA BIM Gathering (2021) that conclude “The most prolific academics in publishing in digital construction appear to be Comiskey and McKane in the area of technology, data sharing and education; Motawa in big data and building performance…”. The novel work in this area was recognised by Ulster University who awarded the team a prestigious Distinguised Teaching Fellowship Award in 2016. The team involved in this area have supervised and supported students in various national/international research events, e.g. a student has recently won a prestigious CIAT International Award.
The University employs over 1,000 suitably qualified and experienced academic staff - 59% have PhDs in their subject field and many have professional body recognition.
Courses are taught by staff who are Professors (25%), Readers, Senior Lecturers (20%) or Lecturers (55%).
We require most academic staff to be qualified to teach in higher education: 82% hold either Postgraduate Certificates in Higher Education Practice or higher. Most academic staff (81%) are accredited fellows of the Higher Education Academy (HEA) by Advanced HE - the university sector professional body for teaching and learning. Many academic and technical staff hold other professional body designations related to their subject or scholarly practice.
The profiles of many academic staff can be found on the University’s departmental websites and give a detailed insight into the range of staffing and expertise. The precise staffing for a course will depend on the department(s) involved and the availability and management of staff. This is subject to change annually and is confirmed in the timetable issued at the start of the course.
Occasionally, teaching may be supplemented by suitably qualified part-time staff (usually qualified researchers) and specialist guest lecturers. In these cases, all staff are inducted, mostly through our staff development programme ‘First Steps to Teaching’. In some cases, usually for provision in one of our out-centres, Recognised University Teachers are involved, supported by the University in suitable professional development for teaching.
Figures correct for academic year 2021-2022.
High quality apartment living in Belfast city centre adjacent to the university campus.
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The Belfast campus is situated in the artistic and cultural centre of the city, the Cathedral Quarter.
Here is a guide to the subjects studied on this course.
Courses are continually reviewed to take advantage of new teaching approaches and developments in research, industry and the professions. Please be aware that modules may change for your year of entry. The exact modules available and their order may vary depending on course updates, staff availability, timetabling and student demand. Please contact the course team for the most up to date module list.
Year: 1
Status: C
This module explores the area of Digital Construction, both in general, and as a means of helping to address pertinent AECFM sector problems or processes identified as requiring advancement. Learners will study contemporary practice and undertake their own research for an identified task. Additionally, there will be a focus on strategic and management capabilities. This module will provide connections to other related subject fields for learners to develop a holistic view of the opportunities resulting from the integration of digital construction processes and/or technologies. The module aims to close the gap in the curricula for supporting the AECFM sector in embracing the Industry 4.0 vision.
Year: 1
Status: C
This module aims to contextualise the role of Business Intelligence and Business Analytics and why we need them. A particular focus will be on how to turn already stored data into valuable information and why this is important. For instance, vast amounts of data regarding company's customers and operations is routinely collected and stored in large corporate data warehouses. This data can be of immense value if properly analysed. Students will explore techniques and tools for data analysis, and presentation of the results to non-technical and managerial staff, in alignment with business strategies. Business intelligence and analytics however, are open to certain ethical and consent issues along with risks. These will be analysed, reviewed and evaluated.
Year: 1
Status: C
The focus of this module is to present an understanding of key data science concepts, tools and programming techniques. Within the arena of data science, the theory behind the approaches of statistics, modelling and machine learning will be introduced emphasising their importance and application to data analysis. The notion of investigative and research skills will also be introduced through a number of problem-solving exercises. The material covered will be contextualised by providing examples of the latest research within the area. Students will also be introduced to programming with Python. They will learn the basics of syntax, and how to configure their development environment for the implementation and testing of algorithms related to data science.
Year: 2
Status: C
The aim of this module is to provide students with insight into the main concepts and principles of Building Information Modelling/Management, vis-à-vis processes, protocols, and enabling technologies. The module is mainly concerned with recent paradigm shift within the AECFM industries worldwide to implement BIM in projects. This module is also heavily inspired by the prospective of UK BIM Framewrok (formerly BIM level 3) and the relevant BIM BS ISO standards. The module will cover strengths and weaknesses, opportunities and threats associated with adopting BIM in AECFM projects and its impact on working procedures as well as business models of AECFM industry. This will be further elaborated in lights of real-life national and international scenarios and case studies.
Year: 2
Status: C
This module is industry focused where students will work collaboratively in teams to develop solutions for specific projects normally within an industrial or practice-based test bed, and/or in association with an appropriate design or industrial organisation. Field trips and regular progress reports are an important aspect of the module. The module is designed to cover topics that address key industry challenges within the themes of Digital Construction. Students are expected to apply the obtained Data Science knowledge in the field of Digital Construction; for example, to develop solutions for challenges of: Lean Construction to reduce waste, Generative Design to achieve Net-Zero emission, product innovation, whole life asset performance, evaluating new relevant technologies (e.g. off-site manufacturing), and achieving energy efficient construction.
Year: 2
Status: C
Deep Learning (DL) and Natural language processing (NLP) are some of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models powering NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. These models can often be trained with a single end-to-end model and do not require traditional, task-specific feature engineering. In this course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a deep excursion into cutting-edge research in deep learning applied to NLP. The final project will involve training a complex neural network and applying it to a large scale NLP problem. On the model side we will cover word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some very novel models involving a memory component. Through lectures and programming assignments students will learn the necessary engineering tricks for making neural networks work on practical problems.
Year: 2
Status: C
This module provides students with a broad understanding of research methods and techniques, and how these can be used to investigate a research problem in any context. Students will be provided with the necessary theoretical foundations in statistical research, including the ability to plan research ethically and conduct a literature review. Students will also be able to record, analyse, interpret and present qualitative and quantitative data appropriately.
We recognise a range of qualifications for admission to our courses. In addition to the specific entry conditions for this course you must also meet the University’s General Entrance Requirements.
At least 2:2 Honours degree (or equivalent) in Architecture, Engineering, Construction or other Built Environment related subject. Previous computer programming skills are not essential requirement. Applicants from other relevant backgrounds (e.g. Data Science and Computer Game) can be considered. Applicants must also provide evidence of English Qualification to the level that meets the University requirements.
English language requirements for international applicants
The minimum requirement for this course is Academic IELTS 6.0 with no band score less than 5.5. Trinity ISE: Pass at level III also meets this requirement for Tier 4 visa purposes.
Ulster recognises a number of other English language tests and comparable IELTS equivalent scores.
In this section
The area of Digital Construction and BIM is expanding in the market and new posts have been created recently in the industry (e.g. BIM manager, Information manager) which need special skills that the course will help learners to develop. These special skills become also essential for career progression. In addition to the construction related knowledge and skills obtained by studying this course, the Data Science knowledge and skills will reinforce the graduates’ abilities and capabilities; hence support their employability in relation to Information Management roles that become more mainstream in the industry. The course offers a unique opportunity for all Built Environment related graduates to study at Masters level and to develop skills applicable to their employment locally or internationally. The course strategy is designed to provide a balance between theory and practice with handson approach on solving real industry problems in collaboration with industry partners. This strategy will provide graduates with key critical thinking and analysis skills that are transferrable and applicable across a wide range of industry sectors. Graduates will be also equipped with the essential skills to pursue a PhD route on graduation if they wish to.
The price of your overall programme will be determined by the number of credit points that you initiate in the relevant academic year.
For modules commenced in the academic year 2023/24, the following fees apply:
| Credit Points | NI/ROI/GB Cost | International Cost |
|---|---|---|
| 5 | £186.65 | £440 |
| 10 | £373.30 | £880 |
| 15 | £559.95 | £1,320 |
| 20 | £746.60 | £1,760 |
| 30 | £1,119.90 | £2,640 |
| 60 | £2,239.80 | £5,280 |
| 120 | £4,479.60 | £10,560 |
| 180 | £6,719.40 | £15,840 |
NB: A standard full-time PGCert is equivalent to 60 credit points per year. A standard full-time PGDip is equivalent to 120 credit points per year.
It is important to remember that costs associated with accommodation, travel (including car parking charges) and normal living will need to be covered in addition to tuition fees.
Where a course has additional mandatory expenses (in addition to tuition fees) we make every effort to highlight them above. We aim to provide students with the learning materials needed to support their studies. Our libraries are a valuable resource with an extensive collection of books and journals, as well as first-class facilities and IT equipment. Computer suites and free Wi-Fi are also available on each of the campuses.
There are additional fees for graduation ceremonies, examination resits and library fines.
Students choosing a period of paid work placement or study abroad as a part of their course should be aware that there may be additional travel and living costs, as well as tuition fees.
See the tuition fees on our student guide for most up to date costs.
We know that choosing to study at university is a big decision, and you may not always be able to find the information you need online.
Please contact Ulster University with any queries or questions you might have about:
For any queries regarding getting help with your application, please select Admissions in the drop down below.
For queries related to course content, including modules and placements, please select Course specific information.
We look forward to hearing from you.
