An NFQ level 8 Bachelor Degree with 2.2 award or equivalent, or a level 7 ordinary degree plus 5 years relevant experience. Applicants may be required to attend interview or to produce an ePortfolio of relevant evidence. Applicants are required to produce evidence of the minimum English language proficiency requirement for overseas applicants i.e. a minimum score of 6.0 in IELTS or equivalent.
This programme services the increasing need for the application of data science methods to the Engineering and Built Environment sectors. To enable professionals to adapt to this rapidly changing context of the 4th Industrial Revolution, they need to understand and embrace the power of analytics and machine learning techniques to interpret, learn from, enrich, predict, and enhance how we design, construct, operate, maintain, and continuously improve our built and natural environments, and the systems, machines and equipment operating in them.
This programme will provide professionals from the Engineering and Construction domains with the skills to maximise the value of their data by analysing & interpreting this information to improve outcomes for industry, society, and individuals.
Core Analytics Module Stack - Stage 1
- Introduction to Analytics for Engineering & Built Environment
- Advanced Data Analytics for Engineering & Built Environment
- Machine Learning for Engineering & Built Environment
Other Core Modules - Stage 3
- Research Methods
- Capstone Experience with Agile Project Management (Minor) or Capstone Experience with Agile Project Management (Major)
Elective Modules - Selection - Stages 1 or 2 depending on availability
- Cloud Computing for Engineering & Built Environment
- Data Analysis using Matlab
- Hetrogeneous Computing Architectures
- Introduction to Programming
- Internet of Things Analytics for Smart Cities and Cognitive Buildings
- Meshless Simulation Methods and Applications
- Software Production
- Statistical Analysis for Engineers
- Virtual & Augmented Reality Applications for Engineering & Built Environment
- Work-Based Learning & Applied Research
An "applied" stream of the programme is also available where students undertake 35 credits of taught modules plus 55 credits of a major Capstone Experience. This stream enables students to focus intensively upon the solution of an industry-based problem or problems while also developing significant research capability, potentially preparing candidates for transfer to level 10 PhD studies.
Half-day or day-long workshops at semester start, middle & end; blended synchronous and asynchronous delivery evenings or by agreement per cohort