Digitization in Facility & Real Estate Management
Niveau
Introduction and consolidation
Learning outcomes of the courses/module
The students are able to:
• Independently analyze and structure data sets as well as present and critically evaluate information
• Independently analyze and structure data sets as well as present and critically evaluate information
Prerequisites for the course
Basic knowledge of spreadsheet & word processing software
Course content
• Basic programming knowledge for data preparation
• Analysis and presentation of information from data sets
• Analysis and presentation of information from data sets
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
Language
English
Number of ECTS credits awarded
4
Semester hours per week
Planned teaching and learning method
Blended Learning
Semester/trimester in which the course/module is offered
2