Application-oriented analysis platforms (elective) (WP)*
Niveau
Master
Learning outcomes of the courses/module
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 suitabil-ity for a specific application.
- Students have gained first application experience with the platforms presented.
Prerequisites for the course
none
Course content
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
Recommended specialist literature
PRIMARY 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)
SECONDARY LITERATURE:
- 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, Farn-ham (ISBN: 978-1491974537)
Assessment methods and criteria
Written exam or seminar thesis
Language
German
Number of ECTS credits awarded
4
Share of e-learning in %
15
Semester hours per week
2.0
Planned teaching and learning method
The following methods are used:
- Lecture with discussion
- Processing of exercises
Semester/trimester in which the course/module is offered
3
Name of lecturer
Mag. Johannes Spiess
Academic year
Key figure of the course/module
WPF.2
Type of course/module
integrated lecture
Type of course
Compulsory elective