t.BA.XX.EXPD.20HS (Explorative Data Analysis) 
Module: Explorative Data Analysis
This information was generated on: 27 April 2024
No.
t.BA.XX.EXPD.20HS
Title
Explorative Data Analysis
Organised by
T IDP
Credits
4

Description

Version: 4.0 start 01 August 2024
 

Short description

 The module Exploratory Data Analysis introduces the basics of descriptive statistics. Students learn how to carry out descriptive data analyses using the statistical software R. This includes preparing, visualizing and describing the data with key figures.

Module coordinator

 Frey Martin (fret)

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
  • Principal component analysis
 
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

Teaching materials

 Lecture notes, lecture and pratical course materials, work and practical worksheets.

Supplementary literature

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

Meier, L. (2020). Wahrscheinlichtsrechnug und Statistik: Eine Einführung für Verständnis, Intuition und Überblick, Springer

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.