Application-oriented analysis platforms (elective)* (WP)*
Niveau
Master
Learning outcomes of the courses/module
The participants:
• Students are familiar with different, application-oriented analysis platforms (e.g. KNIME, RapidMiner, Grafana)
• Students can compare the analysis platforms they have learned with regard to their suitability for a specific applica-tion.
• Students have gained first application experience with the platforms presented.
• Students are familiar with different, application-oriented analysis platforms (e.g. KNIME, RapidMiner, Grafana)
• Students can compare the analysis platforms they have learned with regard to their suitability for a specific applica-tion.
• Students have gained first application experience with the platforms presented.
Prerequisites for the course
None
Course content
• Presentation of different user-oriented analysis platforms (e.g. KNIME, RapidMiner, Grafana)
• Presentation of different cloud solutions for data analysis (e.g. Google Cloud, AWS, Azure)
• Application of the platforms presented using the example of analysis data sets
• Discussion of the different approaches
• Presentation of different cloud solutions for data analysis (e.g. Google Cloud, AWS, Azure)
• Application of the platforms presented using the example of analysis data sets
• Discussion of the different approaches
Recommended specialist literature
• 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)
• Klinkenberg, R., Hofmann, M. (2016): RapidMiner (Ed. 1), Chapman and Hall, Farnham (ISBN: 978-1482205503)
• Lakshmanan, V. (2017): Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning (Ed. 1), O'Reilly Media, Farnham (ISBN: 978-1491974537)
• Klinkenberg, R., Hofmann, M. (2016): RapidMiner (Ed. 1), Chapman and Hall, Farnham (ISBN: 978-1482205503)
• Lakshmanan, V. (2017): Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning (Ed. 1), O'Reilly Media, Farnham (ISBN: 978-1491974537)
Assessment methods and criteria
Seminar paper
Language
English
Number of ECTS credits awarded
4
Semester hours per week
Planned teaching and learning method
Lecture with discussion, processing of exercises, interactive workshop
Semester/trimester in which the course/module is offered
3