Data Science for Engineering & Natural Sciences
Niveau
Master's degree program
Learning outcomes of the courses/module
The participants:
• can identify fundamental areas of application for data collection, data storage, data analysis, and data use in the context of scientific and technical applications
• can understand the particular challenges of this field of application and can identify established best-practice methods in this area
• can design and implement data-based applications in this area themselves, taking domain-specific requirements into account
• can identify fundamental areas of application for data collection, data storage, data analysis, and data use in the context of scientific and technical applications
• can understand the particular challenges of this field of application and can identify established best-practice methods in this area
• can design and implement data-based applications in this area themselves, taking domain-specific requirements into account
Prerequisites for the course
No prerequisites
Course content
The following exemplary topics are covered 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.)
- Sustainability
- Data-driven maintenance (e.g., predictive maintenance, digital twin)
- Data-optimized product design (e.g., designing product characteristics using artificial neural networks)
- Analysis of sensor data (e.g., obstacle detection, obstacle avoidance, prediction, etc.)
- Cloud-based IoT systems (data storage and collection)
- Sensor analysis via Raspberry Pi, Arduino, and wireless 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.)
- Sustainability
- Data-driven maintenance (e.g., predictive maintenance, digital twin)
- Data-optimized product design (e.g., designing product characteristics using artificial neural networks)
- Analysis of sensor data (e.g., obstacle detection, obstacle avoidance, prediction, etc.)
- Cloud-based IoT systems (data storage and collection)
- Sensor analysis via Raspberry Pi, Arduino, and wireless systems
Recommended specialist literature
- VanderPlas, J. (2023): Python Data Science Handbook: Essential Tools for Working with Data (Ed. 2), O'Reilly, (ISBN: 978-1098121228)
- Cady, F. (2017): The Data Science Handbook (Ed. 2), Wiley, Hoboken (ISBN: 978-1119092940)
- Cady, F. (2017): The Data Science Handbook (Ed. 2), Wiley, Hoboken (ISBN: 978-1119092940)
Assessment methods and criteria
Seminar paper
Language
English
Number of ECTS credits awarded
4
Semester hours per week
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