Data Science & Intelligent Analytics PT
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Data Science for Engineering

level of course unit

Master course

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

not specified

course contents

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


course-hours-per-week (chw)


planned learning activities and teaching methods

Lecture with discussion
Interactive workshop
Case studies

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

In-class course

work placement(s)



Mitarbeiterfoto Karsten Böhm
Prof. (FH) Dipl.-Informatiker Karsten Böhm
Director of Studies
+43 5372 71819 133
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