t.BA.XX.EXPD.20HS (Explorative Data Analysis) 
Module: Explorative Data Analysis
This information was generated on: 27 September 2021
No.
t.BA.XX.EXPD.20HS
Title
Explorative Data Analysis
Organised by
T IDP
Credits
4

Description

Version: 1.0 start 01 August 2021
 

Short description

The module Exploratory Data Analysis introduces the basics of descriptive statistics. In this module, the students learn to perform descriptive data analyses, which includes preparing, visualizing and describing the data with key figures using the statistical software R.

Module coordinator

Hofer Christoph (hofc)

Learning objectives (competencies)

Objectives Competences Taxonomy levels
Students develop an understanding of the purpose of a statistical investigation. F, M K1, K2
Students are able to determine meaningful key figures from a given data set and create appropriate, univariate, bivariate and multivarite desired graphs with the help of the statistic software R. F, M K2, K3
Students are able to independently conduct a descriptive analysis of a given dataset. F, M K3, K4
Students are able to read, understand and evaluate graphical data analyses conducted by third parties. F, M K2, K3, K4

Module contents

The students learn descriptive statistics methods to visualize and describe data with statistical key figures.
 
The lessons are divided into the following blocks:
  • Basic concepts of data collection
  • Data types
  • Statistical key figures and graphical representation for univariate data (e.g. location and dispersion parameters, bar chart, histogram, empirical cumulative distribution function, box plot, ...)
  • Statistical key figures and graphical representation for bivariate and multivariate data (e.g. crosstabs, scatter plots, correlation, comparative box plots or bar charts for grouped data)
  • Linear and monotonic data transformations
 
The lab is divided into the following blocks:
  • Introduction to the statistical software R and the development environment RStudio
  • Data structures in R
  • Import and export of data
  • Introduction to R Graphics
  • Functions in R
  • Data preparation in R
  • Alternatives to classic R graphics
  • Reproducible and dynamically customizable descriptive data analysis
  • Introduction to the development of web-based analysis and evaluation tools with R

Teaching materials

lecture notes, introduction to the statistical software R notes, presentation documents, exercises

Supplementary literature

Fahrmeir, L., Künstler, R., Pigeot, I., Tutz, G. (1997). Statistik. Der Weg zur Datenanalyse, Springer.

Stahel, W. (2008). Statistische Datenanalyse. Eine Einführung für Naturwissenschaftler, 5. Auflage, Vieweg, Wiesbaden.

Wollschläger, D. (2). Grundlagen der Datenanalyse mit R, 4. Auflage, Springer

Prerequisites

 

Teaching language

(X) German ( ) English

Part of International Profile

( ) Yes (X) No

Module structure

Type 3a
  For more details please click on this link: T_CL_Modulauspraegungen_SM2025

Exams

Description Type Form Scope Grade Weighting
Graded assignments during teaching semester Short presentation
Midterm exam
Term paper
oral
written
written
10 min.
45 min.
3-5 pages
Grading 5%
10%
20%
End-of-semester exam exam written 90 min. Grading 65%

Remarks

 

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.

Note

Course: Modeling and Simulation of Transport Systems - Vorlesung
No.
t.BA.XX.EXPD.20HS.P
Title
Modeling and Simulation of Transport Systems - Vorlesung

Note

  • No module description is available in the system for the cut-off date of 02 August 2099.
Course: Modeling and Simulation of Transport Systems - Vorlesung
No.
t.BA.XX.EXPD.20HS.V
Title
Modeling and Simulation of Transport Systems - Vorlesung

Note

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