Application-Oriented Analytics Platforms (E) (WP)*
Niveau
Master's program
Learning outcomes of the courses/module
The participants:
• can name different application-oriented analytics platforms (e.g. KNIME, RapidMiner, Grafana)
• can compare the analytics platforms they have learned about in terms of their suitability for a specific use case
• can implement initial applications using the platforms presented
• can name different application-oriented analytics platforms (e.g. KNIME, RapidMiner, Grafana)
• can compare the analytics platforms they have learned about in terms of their suitability for a specific use case
• can implement initial applications using the platforms presented
Prerequisites for the course
no prerequisites
Course content
The following topics are discussed in the course:
- Presentation of different application-oriented analytics platforms (e.g. KNIME, RapidMiner, Grafana)
- Presentation of different cloud solutions for data analysis (e.g. Google Cloud, AWS, Azure)
- Applying the presented platforms using sample analysis datasets
- Discussion of the different approaches
- Presentation of different application-oriented analytics platforms (e.g. KNIME, RapidMiner, Grafana)
- Presentation of different cloud solutions for data analysis (e.g. Google Cloud, AWS, Azure)
- Applying the presented platforms using sample analysis datasets
- Discussion of the different approaches
Recommended specialist literature
- Ramjan, S.; Sunkpho, J. (2025): Utilizing RapidMiner, Python, and R for Data Mining Applications, IGI GLobal
- Shmueli, G.; Bruce, G. C.; Deokar, A. V., Patel, N. R. (2023): Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner, Wiley
- Mishra, A. (2019): Machine Learning in the AWS Cloud: Add Intelligence to Applications with Amazon SageMaker and Amazon Rekognition (Ed. 1), Wiley, Chichester (ISBN: 978-1119556718)
- Shmueli, G.; Bruce, G. C.; Deokar, A. V., Patel, N. R. (2023): Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner, Wiley
- Mishra, A. (2019): Machine Learning in the AWS Cloud: Add Intelligence to Applications with Amazon SageMaker and Amazon Rekognition (Ed. 1), Wiley, Chichester (ISBN: 978-1119556718)
Assessment methods and criteria
Written exam
Seminar paper
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
- Working through practice exercises
- Interactive workshop
- Lecture with discussion
- Working through practice exercises
- Interactive workshop
Semester/trimester in which the course/module is offered
3