Digital Tools & Data Skills
Niveau
Introduction & in-depth study
Learning outcomes of the courses/module
The participants:
• explain basic digital concepts, data structures and interfaces in facility and real estate management.
• use common software tools for structured data capture and management.
• apply simple scripts to analyze, filter and graphically display data.
• analyze data sets for patterns, correlations and sources of error.
• reflect on the benefits, limitations and ethical aspects of digital data processing in building management.
• explain basic digital concepts, data structures and interfaces in facility and real estate management.
• use common software tools for structured data capture and management.
• apply simple scripts to analyze, filter and graphically display data.
• analyze data sets for patterns, correlations and sources of error.
• reflect on the benefits, limitations and ethical aspects of digital data processing in building management.
Prerequisites for the course
Scientific & Empirical Methods
Course content
- Introduction to digital tools and data management
- Introduction to programming and databases
- Data quality, error detection and cleaning
- Cloud and collaboration tools for data projects
- Data protection, data security and ethical aspects of data processing
- Introduction to programming and databases
- Data quality, error detection and cleaning
- Cloud and collaboration tools for data projects
- Data protection, data security and ethical aspects of data processing
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
3
Semester hours per week
Planned teaching and learning method
Lectures, discussions, case studies, group work
Semester/trimester in which the course/module is offered
2