PhD students and postdoctoral researchers of all areas
This advanced course is intended for researchers working on empirical and quantitative questions. During the training, the participants will have the opportunity to test and consolidate what they have learned with practical exercises, so that they can later work independently with the learned statistical methods.
Rationale of the course
This course is based on the course "Introduction to R", gives a comprehensive overview of the field of statistics and teaches the participants the most important methods of statistical analysis in R. Participants will be introduced to analyses from three thematic blocks: multivariate statistics, time series analysis and data mining.
Aim of the course
Participants will learn how to use methods of multivariate statistics to uncover patterns and relationships in data. Therefore, the course contains an introduction to time series analysis and central smoothing and forecasting methods as well as an overview of machine learning algorithms and a practical presentation of the typical workflow of a machine learning project in R.
- Introduction to multivariate statistical methods
- Central smoothing and forecasting methods of time series analyses
- Presentation of typical data minig algorithms
- Demonstration and interpretation of different data mining metrics for performance measurement
Trainer - Nico Frieß (eoda GmbH)
Nicolas Frieß joined eoda as a Data Scientist after building expertise in data analytics as an active researcher in the fields of environmental sciences and geoinformatics. His work focuses on machine learning projects in industry and manufacturing as well as software development in the area of productive Shiny applications.
- 10 July 2023, 09:00 – 12:00 h
- 12 July 2023, 09:00 – 12:00 h
- 13 July 2023, 09:00 – 12:00 h
- 14 July 2023, 09:00 – 12:00 h
Please register via the »Graduates Virtual Campus«: www.b-tu.de/elearning/graduates
Bemerkung zum Termin:
Eine Woche vor Kursbeginn erhalten Sie den Einwahl-Link für die Veranstaltung. Melden Sie sich über das GRS-Kursportal an.
ZE Graduate Research School (GRS)
T +49 (0) 355 69-3479