851017 Exploratory data analysis with R (in Eng.)
Diese Seite ist erreichbar unter:
- Vorlesung und Seminar
- Vortragende/r (Mitwirkende/r)
- Laa, Ursula
- Angeboten im Semester
- Wintersemester 2021/22
- Unterrichts-/ Lehrsprachen
Data science methods are becoming increasingly important in scientific research as well as business applications. R is a flexible and powerful statistics program that is freely available and provides a large library of methods for the exploration of data. The course will teach exploratory data analysis in R, with a focus on workflows, different types of real-world data, visualization and reproducible analysis.
- Working with data: concept of tidy data, data exploration and wrangling with the tidyverse packages
- Data Visualization: the grammar of graphics, visualization of multivariate data, animated and interactive graphics
- Identification and handling of missing values and outlying points
- Exploring spatial and temporal data
- Application of exploratory methods such as principal components analysis and clustering
- Reproducible reporting with R Markdown
- Interactive dashboards with R Shiny
- Inhaltliche Voraussetzungen (erwartete Kenntnisse)
Basic knowledge of statistics and R is recommended, for example Statistics with R (851309, 851402) or First Steps with R (851016).
By completing this course, students should be able to perform exploratory data analyses for a range of data types and formats. The students have acquired a repertoire of explorative methods, are able to name and describe them, understand their methodological assumptions, and have the competence to apply them to concrete problems with the use of statistical software. The skills comprise generating reproducible reports and interactive dashboards, and to interpret the results.
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