Timber Construction (Bachelor Professional)

The Timber Construction Professional course is a training and further education system tailored to modern timber construction. The modular structure of the topics and the chronological sequence allow the training to be organised very flexibly and completed alongside work. The concept covers the content of the preparatory courses for the master craftsman qualification in timber construction in accordance with the qualification examination regulations for master craftsmen in timber construction, as well as the academic training for a Bachelor Professional degree.

Business Informatics and Digital Transformation (Bachelor)

Increasing digitalisation poses new challenges for companies. The Bachelor’s degree in Business Informatics & Digital Transformation enables students to tackle these kinds of complex problems with innovative approaches. As experts in the areas of IT and business administration, they combine expertise from two worlds. Students convince people as an IT specialist and learn how to combine technical and economic know-how in the development of professional IT products and services. A practical mix of technical and economic topics enables them to meet the challenges of the digital world professionally.

Smart Digital Construction

Training on BIM methodology for construction project management, collaborative BIM models for planning, execution and maintenance, and data analysis for decision-making in construction processes.

Introduction to Virtual and Augmented Reality

Introductory training on virtual, augmented and mixed reality, including concepts, tools, frameworks, devices, human-centred development, and simple immersive application development.

Introduction to QGIS

Training on the fundamentals of GIS and QGIS, including spatial data processing, cartographic projections, map customisation, spatial analysis, thematic map production and layout preparation.

Advanced Data Analysis and Forecasting

Training in exploratory data analysis, dimensionality reduction, clustering, machine learning techniques and time-series forecasting using R for data-driven analysis and prediction.

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.

Project Number:
101104680-HABITABLE-ERASMUS-EDU-2022-PEX-COVE