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Risk-Based Decision Making and Statistical Data Analysis

Module name (EN): Risk-Based Decision Making and Statistical Data Analysis
Degree programme: Computer Science and Communication Systems, Bachelor, ASPO 01.10.2017
Module code: KIB-ERSD
Hours per semester week / Teaching method: 2V+2P (4 hours per week)
ECTS credits: 4
Semester: 5
Mandatory course: no
Language of instruction:
German
Assessment:
Written exam
Curricular relevance:
KI626 Computer Science and Communication Systems, Bachelor, ASPO 01.10.2014, semester 5, optional course, technical
KIB-ERSD Computer Science and Communication Systems, Bachelor, ASPO 01.10.2017, semester 5, optional course, technical
PIBWI94 Applied Informatics, Bachelor, ASPO 01.10.2011, semester 5, optional course, informatics specific
PIB-ERSD Applied Informatics, Bachelor, ASPO 01.10.2017, semester 5, optional course, informatics specific
Workload:
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 120 hours (equivalent to 4 ECTS credits).
There are therefore 75 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Melanie Kaspar, M.Sc.
Lecturer: Melanie Kaspar, M.Sc.

[updated 10.11.2016]
Learning outcomes:
After completing this course, students will be able to analyze and evaluate large amounts of data and statistically evaluate it using software.
In addition, they will be able to make statements on the reliability and statistical certainty of their evaluation results.

[updated 26.02.2018]
Module content:
1. Risk-Based Decision Making:
   1.1 Bayesian networks
   1.2 Decision trees
   1.3 Boolean reliability theory
   1.4 Markov chains
   1.5 Statistical decisions: hypothesis testing and estimates
   1.6 Decisions in contingency tables
   1.7 Software: SPSS, Answertree
   1.8 Case studies
2. Statistical data analysis - data mining with statistical methods
   2.1 Scale types of random features
   2.2 Statistical measures for data sets
   2.3 Correlations
   2.4 Cluster analysis technique data aggregation
   2.5 Probit analyses
   2.6 Software: SPSS, Clementine
   2.7 Case studies


[updated 26.02.2018]
Teaching methods/Media:
100% of the lecture will take place in the PC lab AMSEL "Angewandte Mathematik, Statistik und eLearning". Computer-supported practical case studies will be carried out here using SPSS and R.
  
In addition, the eLearning system MathCoach-Statistik (AMSEL PC laboratory 5306) will be used. Students must complete homework and exercises using this system.

[updated 24.02.2018]
Recommended or required reading:
Lecture notes: B.Grabowski: Entscheidungen unter Risiko und statistische Datenanalyse, HTW, 2010
 
J.Janssen, W. Laaz: Statistische Datenanalyse mit SPSS, Springer, 2009
 
Handbooks: Answertree, Clementine, SPSS


[updated 19.02.2018]
Module offered in:
WS 2020/21 (probably), WS 2019/20
[Sun Jul  5 14:21:42 CEST 2020, CKEY=keurusd, BKEY=ki2, CID=KIB-ERSD, LANGUAGE=en, DATE=05.07.2020]