Data Analytics and Process Modelling
Datenanalyse und Prozessmodellierung
6 CPWinterENFÜS
Prerequisites
Recommended, as written
Good knowledge of advanced mathematics as well as physics or a basic engineering subject (e.g. electrical engineering, mechanics or thermodynamics) at the university bachelor's level
Contents
• Basic Descriptive Statistics and Data Visualisation • Theoretical Foundation for Statistical Analysis • Regression Analysis • Design of Experiments • Input-State-Output Systems • Process Modelling and System Identification • Soft Sensors and Digital Twins
Learning outcomes
The students learn to develop, evaluate and validate models, design experiments and analyse data. The focus is placed on practical applications, particularly in energy and process technology. Students are also familiarised with soft sensors and digital twins.
Assessment
• Written exam (120 min)
Literature (3)
- Shardt, Yuri: Statistics for Chemical and Process Engineers. A Modern Approach. 2nd edition (2022). DOI: 10.1007/978-3-030-83190-5.
- Shardt, Yuri (2023): Using MATLAB to Solve Statistical Problems. DOI: 10.1007/978-3-031-40299-9.
- Shardt, Yuri (2024): Using Excel to Solve Statistical Problems. DOI: 10.1007/978-3-031-65449-7.
Remarks
Students are expected to bring a laptop to the exercises.
Source: BTU's module description · retrieved 14 Oct 2026Original