Course Title: Postgraduate Certificate in Digital Construction Analytics / Engineering Analytics
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, the programme has developed modules in machine learning, data and statistical analysis, programming and BIM . This course 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 our built and natural environments, and the systems, machines and equipment operating in them.
This level 9 programme specifically targets the retention and career progression of staff through the development of new and valuable skills in the area of analytics. This course enables students to expand their job and career opportunities, as it seeks to create professionals who can leverage a variety of analytics tools, statistical methods, data mining techniques, and classification methods to provide insights into the vast collation of data being produced by construction organisations daily.
Students will be required to own or have significant access to a PC or laptop that meets the specifications identified for Revit 2023 - Value
2.2 in an engineering, built environment or related level 8 programme
or
equivalent knowledge as assessed via TU Dublin's Recognition of Prior Learning processes.
Semester 1 (January - May)
- Introduction to Analytics for the Built Environment - This module will provide students with an in-depth understanding of the basic principles of data analytics and their use in Engineering & Built Environment contexts.
- Introduction to Programming - This module presents a comprehensive introduction to programming, with a particular emphasis on object-oriented programming. Topics include basic programming skills as well as in-depth coverage of the object-oriented paradigm.
- Statistics Analysis for Engineers - The aim of this module is to instil in students an awareness of, and competency in, statistical methods, to equip them with the tools to critically analyse research papers and data, and introduce them to several statistical software packages to enable them to analyse data.
Semester 2 ( September - December)
- Machine Learning for the Built Environment - The aim of this module is to provide students with an in-depth understanding of the principles of machine learning, and the problems and solutions suitable for machine learning techniques.
- Advanced Data Analytics for the Built Environment - The aim of this module is to provide students with an in-depth understanding of the data analytics pipeline from obtaining data from various sources, importation, wrangling, exploratory data analysis, cleaning and visualisation, and the problems and solutions suitable at each stage.
- Choice BIM Elective i.e. BIM in Architecture Advanced Fundamentals or BIM in Mechanic and Electrical Advanced Fundamentals or BIM for Construction Management Advanced Fundamentals.
Runs Monday and Wednesday evenings from 18:00-21:00 with additional asynchronous directed and self-directed learning outside of these times.
Students will be expected to attend classes in Bolton Street on Wednesday evenings (18:00 - 20:00) for the Statistical Analysis for Engineers. This module is delivered through a blended format i.e. 6 classes in Bolton Street and 6 classes online. All other modules will be delivered online.
Students should plan to assign 10-14 hours per week for study (which includes the scheduled class times) during the two semesters.
This course is funded under the Springboard+ initiative in 2024. Applications should be made via the Springboard+ website, subject to meeting the eligibility criteria.
Further Information: For further information please email springboard@tudublin.ie.
Non-EU Students
Non-EU students, not resident in Ireland, are not eligible to apply for part time programs that are delivered on-campus. Applications for part time courses that require on-campus attendance will not be processed and the application fee will not be refunded.