No-Code & Low-Code Analysis Platforms
Niveau
Master's degree program
Learning outcomes of the courses/module
The participants:
• can identify various application-oriented analysis platforms (e.g., KNIME, RapidMiner, Grafana)
• can compare the analysis platforms they have learned about with regard to their suitability for a particular use case
• can implement initial applications using the presented platforms
• can identify various application-oriented analysis platforms (e.g., KNIME, RapidMiner, Grafana)
• can compare the analysis platforms they have learned about with regard to their suitability for a particular use case
• can implement initial applications using the presented platforms
Prerequisites for the course
No prerequisites
Course content
The following topics are covered in the course:
- Presentation of various application-oriented analysis platforms (e.g., KNIME, RapidMiner, Grafana)
- Presentation of various cloud solutions for data analysis (e.g., Google Cloud, AWS, Azure)
- Applying the presented platforms using example analysis datasets
- Discussion of the various approaches
- Presentation of various application-oriented analysis platforms (e.g., KNIME, RapidMiner, Grafana)
- Presentation of various cloud solutions for data analysis (e.g., Google Cloud, AWS, Azure)
- Applying the presented platforms using example analysis datasets
- Discussion of the various 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 or 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 on practice exercises
- Interactive workshop
- Lecture with discussion
- Working on practice exercises
- Interactive workshop
Semester/trimester in which the course/module is offered
3