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Application-oriented analysis platforms (elective)*
level of course unit
MasterLearning outcomes of course unit
The following learning outcomes are developed in the course:
- Students are familiar with a range of 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 a range of 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 and co-requisites
none
course contents
The following content is discussed in the course:
- 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 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
recommended or required reading
PRIMÄRLITERATUR:
- 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)
SEKUNDÄRLITERATUR:
- 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)
- 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)
SEKUNDÄRLITERATUR:
- 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 thesis
language of instruction
Germannumber of ECTS credits allocated
4eLearning quota in percent
15course-hours-per-week (chw)
2planned learning activities and teaching methods
The following methods are used:
- Lecture with discussion
- Processing of exercises
- Interactive workshop
- Lecture with discussion
- Processing of exercises
- Interactive workshop
