Data Science & Intelligent Analytics PT
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Internet of Things (elective)

level of course unit

Master's course

Learning outcomes of course unit

- know basic IOT architectures.
- know methods of data generation.
- know the basics of data transmission.
- know the options of data storage.
- know the forms of data visualization.
- understand challenges of data security.

prerequisites and co-requisites

not applicable

course contents

- IoT architecture (e.g. reference models)
- Requirements for IOT systems
- IOT data transmission protocols
- Use of IOT in an industrial context (examples)
- Basics of sensor technology
- Basics of embedded systems

- Procedure for implementing IOT
- Prototypical implementation of IOT
- Selection of sensors
- Data collection, visualization and evaluation
- Challenges in implementation

recommended or required reading

Perry L.; Internet of Things for Architects: Architecting IoT solutions by implementing sensors, communication infrastruc-ture, edge computing, analytics, and security; Birmingham; 2018
Sinclair B.; IoT Inc: How Your Company Can Use the Internet of Things to Win in the Outcome Economy; 2017
Thomas O., Nüttgens M., Fellmann M. (editor); Smart Service Engineering: Konzepte und Anwendungsszenarien für die digitale Transformation; Wiesbaden; 2017

assessment methods and criteria

Written exam or seminar thesis

language of instruction


number of ECTS credits allocated


eLearning quota in percent


course-hours-per-week (chw)


planned learning activities and teaching methods

Lecture, individual work with software, group work, presentation and discussion of tasks

semester/trimester when the course unit is delivered


name of lecturer(s)

Prof. (FH) Dipl.-Ing. Thomas Schmiedinger, PhD

year of study

2. study year

recommended optional program components

not applicable

course unit code


type of course unit

integrated lecture

mode of delivery


work placement(s)