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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

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

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

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

Type of course/module

Type of course