Studienhandbuch | KV Explorative Data Analysis in R

Inhalt

Es ist eine neuere Version 2026W dieser LV im Curriculum Master's programme Social Economics 2026W vorhanden.

Workload	Education level	Study areas	Responsible person	Hours per week	Coordinating university
2 ECTS	B1 - Bachelor's programme 1. year	Statistics	Andreas Futschik	1 hpw	Johannes Kepler University Linz

Detailed information

Original study plan

Bachelor's programme Statistics and Data Science 2025W

Learning Outcomes

Competences
Students are able to work with the statistical programming language R and use it for data cleaning, and explorative data analysis.

Skills	Knowledge
Application for R for exploratory data analysis (k3) Application of R for data cleaning and data transformation (k3) Application of R for simple programming tasks (k3)	Installing the R-Environment Basic data types and commands Structure of the R-Language R for data cleaning Functions and commands for exploratory data analysis including visualization

Criteria for evaluation

homework and exam

Methods

presentation by the instructor

computer work in class

presentation of homework by students and discussion

Language

German

Study material

Hatzinger, Hornik und Nagele(2014).R: Einführung in die angewandte Statistik

Changing subject?

Further information

The course can be taken in parallel to basic courses in statistics also by students from other programs.

Earlier variants

They also cover the requirements of the curriculum (from - to)
551GRUSEDRK18: KV Explorative Data Analysis in R (2018W-2019S)

On-site course
Maximum number of participants	40
Assignment procedure	Assignment according to priority