t.BA.DS.PM2.20HS (Data Processing with R) 
Module: Data Processing with R
This information was generated on: 27 September 2021
Data Processing with R
Organised by


Version: 1.0 start 01 August 2020

Short description

Preparing, cleansing and visualising data are central tasks of a data scientist. In this module students train and consolidate the necessary skills in project teams, which they have acquired in the modules Explorative Datenanalyse and Datenbanken.

Module coordinator

Hofer Christoph (hofc)

Learning objectives (competencies)

Objectives Competences Taxonomy levels
Students are able to import data from various file formats (text, CSV, Excel, ...) and data formats (JSON, XML, ...) into a suitable data structure of the statistical software R and can retrieve data from databases F, M K3
Students are able to use the statistics software R to clean up data sets and find outliers and errors, remove duplicates and mark missing values and impute them by using simple methods. F, M K3
Students are able to use the statistical software R to transform, sort, filter, group, aggregate and combine data for specific questions and to generate useful variables from existing variables. F, M K3
Students are able to write functions  for routine analyses with the statistics software R
and are able to automate data preparation, cleansing and visualisation (automated reporting).
F, M K3
Students are able to  create shiny interfaces for standardised data formats using the statistics software R, which allows third parties to carry out simple data analyses independently via a GUI. F, M K3
The students are able to work together in a team in a goal-oriented manner, support each other and take responsibility for the developed results in the joint project. SO K3
Students are able to identify knowledge gaps to solve problems in a project  and are able to  provide the necessary information. SE K3

Module contents

Students expand their techniques learned in the modules Explorative Datenanalyse and Datenbanken using practical examples. For this purpose, students will work in small project teams on various tasks of increasing complexity. In addition to consolidating their specialist skills, students will also be encouraged to develop interdisciplinary skills such as teamwork and working through gaps in knowledge (research). Students have to record the course of the project (including critical reflection) and record the results in writing or oral form.

Teaching materials


Supplementary literature




Teaching language

(X) German ( ) English

Part of International Profile

( ) Yes (X) No

Module structure

Type 4
  For more details please click on this link: T_CL_Modulauspraegungen_SM2025


Description Type Form Scope Grade Weighting
Graded assignments during teaching semester various written and oral per project marking 100%


Individual performance can also influence individual grades in group work, i.e. not all group members must always receive the same grade.

Legal basis

The module description is part of the legal basis in addition to the general academic regulations. It is binding. During the first week of the semester a written and communicated supplement can specify the module description in more detail.
Course: Data Processing with R - Praktikum
Data Processing with R - Praktikum


  • No module description is available in the system for the cut-off date of 02 August 2099.