Digital Tools & Data Skills (E)
Niveau
Introductory & advanced
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 visualize data.
• analyze datasets for patterns, relationships, and sources of error.
• reflect on the benefits, limitations, and ethical aspects of digital data processing in building management.
• transfer digital concepts and methods to their own professional fields of practice in facility and real estate management and reflect on concrete application potential in their respective company context.
• analyze operational data structures and processes from their work environment and identify opportunities for optimization through the targeted use of digital tools.
• assess the feasibility of implementing digital solutions, taking into account organizational conditions, resources, and existing system landscapes.
• reflect on the added value of data-based decision-making foundations for strategic and operational tasks within their professional area of responsibility.
• develop a deeper understanding of digital transformation processes within their own company and derive individual perspectives for further training and development from them.
• 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 visualize data.
• analyze datasets for patterns, relationships, and sources of error.
• reflect on the benefits, limitations, and ethical aspects of digital data processing in building management.
• transfer digital concepts and methods to their own professional fields of practice in facility and real estate management and reflect on concrete application potential in their respective company context.
• analyze operational data structures and processes from their work environment and identify opportunities for optimization through the targeted use of digital tools.
• assess the feasibility of implementing digital solutions, taking into account organizational conditions, resources, and existing system landscapes.
• reflect on the added value of data-based decision-making foundations for strategic and operational tasks within their professional area of responsibility.
• develop a deeper understanding of digital transformation processes within their own company and derive individual perspectives for further training and development from them.
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 data cleansing
- Cloud and collaboration tools for data projects
- Data protection, data security, and ethical aspects of data processing
- Transferring the digital fundamentals covered to specific operational questions in each participant's company environment
- Analysis and optimization of existing digital processes and system landscapes in facility and real estate management
- Practice-based reflection on integrating new digital solutions into ongoing work processes, taking organizational conditions into account
- Sharing experiences and discussing best practices for digital transformation projects from the participants' professional contexts
- Development of small, practice-oriented implementation projects directly related to participants' own work environments
- Introduction to programming and databases
- Data quality, error detection, and data cleansing
- Cloud and collaboration tools for data projects
- Data protection, data security, and ethical aspects of data processing
- Transferring the digital fundamentals covered to specific operational questions in each participant's company environment
- Analysis and optimization of existing digital processes and system landscapes in facility and real estate management
- Practice-based reflection on integrating new digital solutions into ongoing work processes, taking organizational conditions into account
- Sharing experiences and discussing best practices for digital transformation projects from the participants' professional contexts
- Development of small, practice-oriented implementation projects directly related to participants' own work environments
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
6
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