Data Science for Engineering
level of course unit
Learning outcomes of course unit
Students are familiar with the usage areas of data acquisition, data storage, data analysis and data usage within the context of engineering-science and IoT applications. They understand the special challenges in this usage area and are familiar with established best practice methods. Furthermore, they are able to independently design and implement data-based applications in this area while taking domain-specific requirements into account.
prerequisites and co-requisites
Students acquire detailed knowledge of techniques and tools of data science in the area of engineering sciences and consolidate their knowledge with datasets from various engineering sciences (sensor technology, robotics, telemetry). The following topical fields are discussed in detail:
-Data-driven maintenance (e.g. predictive maintenance, digital twin)
-Data-optimized product design (e.g. design of product characteristics through KNN)
-Evaluation of sensor data (e.g. obstacle detection, obstacle avoidance, pre-diction, etc.)
-Cloud-based IoT systems (data storage and collection)
-Sensor evaluation via Raspberry Pi, Arduino, radio systems
-Predictive data evaluation via neuronal networks
recommended or required reading
- Cady, F. (2017) The Data Science Handbook. 2. Auflage, Wiley, Hoboken (ISBN: 978-1119092940).
- Heinrich, B.; Linke, P.; Glöckler, M. (2017) Grundlagen Automatisierung: Sensorik, Regelung, Steuerung. 2. Auflage, Springer Vieweg, Wiesbaden (ISBN: 978-3658175818).
- Tränkler, H.-R.; Reindl, L. M. (2015) Sensortechnik: Handbuch für Praxis und Wissenschaft. 2. Auflage, Springer Vieweg, Wiesbaden (ISBN: 978-3642299414).
- Serpanos, D.; Wolf, M. (2017) Internet-of-Things (IoT) Systems: Architectures, Algorithms, Methodologies. 1. Auflage, Springer, Berlin (ISBN: 978-3319697147).
- Kranz, M. (2016) Building the Internet of Things: Implement New Business Models, Disrupt Competitors, Transform Your Industry. 1. Auflage, Wiley, Chichester (ISBN: 978-1119285663).
assessment methods and criteria
Final exam or seminar thesis
language of instruction
number of ECTS credits allocated
planned learning activities and teaching methods
Lecture with discussion
semester/trimester when the course unit is delivered
name of lecturer(s)
Head of studies
year of study
recommended optional program components
course unit code
type of course unit
mode of delivery