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Data Science for Engineering & Natural Sciences
level of course unit
Master's courseLearning 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.
- 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
- 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
Englishnumber of ECTS credits allocated
4eLearning quota in percent
30course-hours-per-week (chw)
1.75planned learning activities and teaching methods
The following methods are used:
- Lecture with discussion
- Interactive workshop
- Case studies
- Lecture with discussion
- Interactive workshop
- Case studies
