Data Science for Engineering & Natural Sciences
Niveau
Master's course
Learning outcomes of the courses/module
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 for the course
3rd semester: No prerequisites
Course content
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 specialist literature
English version available soon
Assessment methods and criteria
Seminar thesis
Language
English
Number of ECTS credits awarded
4
Share of e-learning in %
30
Semester hours per week
1.75
Planned teaching and learning method
The following methods are used:
- Lecture with discussion
- Interactive workshop
- Case studies
- Lecture with discussion
- Interactive workshop
- Case studies
Semester/trimester in which the course/module is offered
3
Name of lecturer
Prof. (FH) Dr. Lukas Huber
Academic year
Key figure of the course/module
MDS.6
Type of course/module
integrated lecture
Type of course
Compulsory
Internship(s)
none