Data Science & Intelligent Analytics PT
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Data Science for Engineering & Natural Sciences

level of course unit

Master's course

Learning outcomes of course unit

The following skills are developed in the course:

- Students know the basic application areas of data collection, data storage, data analysis and data use in the context of scientific and technical applications.
- Students understand the special challenges of this field of application and are familiar with established best practice methods in this area.
- This enables students to design and implement data-based applications in this area themselves, taking into account domain-specific requirements.

prerequisites and co-requisites

3rd semester: No prerequisites

course contents

The following exemplary contents are discussed in the course:

- Biology (e.g. genome research, medical diagnostic procedures, etc.)
- Physics (e.g. object recognition through image data processing, etc.)
- Chemistry (e.g. processing of data-intensive experiments, etc.)
- Data-driven maintenance (e.g. predictive maintenance, Digital Twin)
- Data-optimized product design (e.g. design of product properties by KNN)
- Evaluation of sensor data (e.g. obstacle detection, obstacle avoidance, prediction, etc.)
- Cloud-based IoT systems (data storage and collection) - sensor evaluation via Raspberry Pi, Arduino, radio systems

recommended or required reading

English version available soon

assessment methods and criteria

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

The following methods are used:

- Lecture with discussion
- Interactive workshop
- Case studies

semester/trimester when the course unit is delivered


name of lecturer(s)

Prof. (FH) Dipl.-Informatiker Karsten Böhm

course unit code


type of course unit

integrated lecture

mode of delivery


work placement(s)