Application-oriented analysis platforms (elective)*

Master

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.

none

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

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)

Seminar thesis

German

4

15

2.0

The following methods are used:

- Lecture with discussion
- Processing of exercises
- Interactive workshop

3

Mag. Johannes Spiess

WPF.2

integrated lecture

Compulsory

none