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Risk-Based Decision Making and Statistical Data Analysis

Module name (EN):
Name of module in study programme. It should be precise and clear.
Risk-Based Decision Making and Statistical Data Analysis
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Applied Informatics, Bachelor, ASPO 01.10.2022
Module code: PIB-ERSD
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P221-0107
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
2V+2P (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
4
Semester: 5
Mandatory course: no
Language of instruction:
German
Assessment:
Written exam

[updated 19.02.2018]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

KI626 Computer Science and Communication Systems, Bachelor, ASPO 01.10.2014 , semester 5, optional course, technical
KIB-ERSD (P221-0107) Computer Science and Communication Systems, Bachelor, ASPO 01.10.2021 , semester 5, optional course, technical
KIB-ERSD (P221-0107) Computer Science and Communication Systems, Bachelor, ASPO 01.10.2022 , semester 5, optional course, technical
PIBWI94 (P221-0106) Applied Informatics, Bachelor, ASPO 01.10.2011 , semester 5, optional course, informatics specific
PIB-ERSD (P221-0107) Applied Informatics, Bachelor, ASPO 01.10.2022 , semester 5, optional course, informatics specific
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
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 2024/25, WS 2023/24, WS 2022/23, WS 2021/22, WS 2020/21, ... 
[Fri Dec 27 17:53:29 CET 2024, CKEY=keurusd, BKEY=pi2, CID=PIB-ERSD, LANGUAGE=en, DATE=27.12.2024]