EventoWeb
Zürcher Hochschule für Angewandte Wissenschaften
Not registered
(Friday, March 29, 2024 4:21:25 PM)
t.BA.XX.EXPD.20HS (Explorative Data Analysis)
Module: Explorative Data Analysis
This information was generated on: 29 March 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
Additional available versions:
3.0 start 01 February 2021
,
1.0 start 01 August 2021
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.