Data Analytics
Niveau
In-depth study
Learning outcomes of the courses/module
The participants:
• identify and structure relevant data sources in real estate systems.
• analyze real estate data with suitable tools.
• develop data-based decision models and critically assess their significance.
• reflect on ethical and data-protection aspects in handling operational and usage data and develop proposals for a responsible data strategy.
• identify and structure relevant data sources in real estate systems.
• analyze real estate data with suitable tools.
• develop data-based decision models and critically assess their significance.
• reflect on ethical and data-protection aspects in handling operational and usage data and develop proposals for a responsible data strategy.
Prerequisites for the course
Digital Tools & Data Skills, Building Technology - Electrical & Safety Systems,
Course content
- Fundamentals of data analytics in real estate and facility management
- Data types and data sources
- Data preparation, modeling and visualization
- Key figures, KPIs and dashboards for FM and CREM processes
- Data protection, data security and ethical aspects in data analysis
- Data types and data sources
- Data preparation, modeling and visualization
- Key figures, KPIs and dashboards for FM and CREM processes
- Data protection, data security and ethical aspects in data analysis
Recommended specialist literature
- Amos, D., Bader, D., Jablonski, J., & Heisler, F. (2021). Python basics: A practical introduction to Python 3 (Revised and updated 4th edition). Real Python.
- Matthes, E. (2023). Python crash course: A hands-on, project-based introduction to programming (3rd edition). No Starch Press.
- Runkler, T. A. (2025a). Data Analytics: Models and Algorithms for Intelligent Data Analysis - A Comprehensive Introduction (4th ed. 2025). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-45951-2
- Runkler, T. A. (2025b). Data Analytics: Models and Algorithms for Intelligent Data Analysis - A Comprehensive Introduction (4th ed. 2025). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-45951-2
- Matthes, E. (2023). Python crash course: A hands-on, project-based introduction to programming (3rd edition). No Starch Press.
- Runkler, T. A. (2025a). Data Analytics: Models and Algorithms for Intelligent Data Analysis - A Comprehensive Introduction (4th ed. 2025). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-45951-2
- Runkler, T. A. (2025b). Data Analytics: Models and Algorithms for Intelligent Data Analysis - A Comprehensive Introduction (4th ed. 2025). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-45951-2
Assessment methods and criteria
Portfolio assessment
Language
English
Number of ECTS credits awarded
5
Semester hours per week
Planned teaching and learning method
Lectures, discussions, exercises, case studies, group work
Semester/trimester in which the course/module is offered
5