Modulhandbuch (Module manual)

M.184.3365 Information Technology for Decision Making
(Information Technology for Decision Making)
Koordinator (coordinator): Prof. Dr. Guido Schryen
Ansprechpartner (contact): Carina Uhde (carina.trimborn[at]uni-paderborn.de)
Credits: 5 ECTS
Workload: 150 Std (h)
Semesterturnus (semester cycle): SoSe
Studiensemester (study semester): 3-6
Dauer in Semestern (duration in semesters): 1
Lehrveranstaltungen (courses):
Nummer / Name
(number / title)
Art
(type)
Kontaktzeit
(contact time)
Selbststudium
(self-study)
Status (P/WP)
(status)
Gruppengröße
(group size)
a) K.184.33651 / Information Technology for Decision Making Block 75 Std (h) 75 Std (h) P
Wahlmöglichkeiten innerhalb des Moduls (Options within the module):
Keine
Empfohlene Voraussetzungen (prerequisites):
  • ​Working knowledge of Microsoft Excel, Fundamental Knowledge in Economics and Accounting
  • sufficient knowledge of the English​ language
  • Inhalte (short description):

    Part 1: Database Management Systems

    Upon completion of prescribed work for this part of the course, the student should be able to:

  • Discuss relational database management systems (DBMS)
  • Explain the difference between redundancy and duplication
  • Eliminate redundancy through table splitting
  • Eliminate repeating groups in databases
  • Effectively create a DBMS with tables, relationships and queries in MS Access
  •  

    Part 2: Decision Support Systems and Traditional Spreadsheet Modeling

    Upon completion of prescribed work for this part of the course, the student should be able to:

  • Demonstrate ability to collaborate within a diverse group of students and make complex decisions
  • Effectively collect data and use FONDA (Filtering, Organizing, Normalizing, Deciding, and Analyzing)
  • Effectively use SWOT analysis to organize data into Strenegths/Opportunities and Weaknesses/Threats
  • Construct a euclidean model to classify alternatives into four quadrants (Low Risk-Low Return, Low Risk-High Return, High Risk-Low Return, and High Risk-High Retrn)
  • Effectively formulate recommendations and write a comprehensive group consulting report
  •  

    Part 3: Decision Support Systems and Natural Language Programming

    Upon completion of prescribed work for this part of the course, the student should be able to:

  • Discuss decision support systems (DSS)
  • Perform what-if analysis
  • Perform trial and error
  • Perform goal seeking
  • Formulate mathematical optimization problems
  • Effectively use SOLVER to solve optimization problems in MS Excel
  •  

    Part 4: Decision Support Systems and Influence Diagramming

    Upon completion of prescribed work for this part of the course, the student should be able to:

  • Discuss natural language programming (NLP)
  • Discuss non-procedural programming languages
  • Explain the role of NLP in financial and operational modeling
  • Write natural language programs
  • Effectively use NLP software like D-code and dynamic data exchange
  •  

    Part 5: Strategic Information Systems

    Upon completion of prescribed work for this part of the course​, the student should be able to:

  • Discuss influence diagramming (ID)
  • Explain the difference between constant, variable, self-reference variable, and series in ID
  • Effectively model and solve ID problems with D-cide
  • Effectively use dynamic data exchange between D-cide and excel

  • Part 6: Knowledge Engineering and Expert Systems

    Upon completion of prescribed work for this part of the course, the student should be able to:

  • Discuss knowledge engineering and expert systems (ES)
  • Explain the difference between knowledge representation techniques (i.e., decision tables, decision trees, and structured English)
  • Explain rule-based ESs
  • Represent rule-based ESs with decision trees
  • Effectively use ES software like B-wise
  • Lernergebnisse (learning outcomes):
    Fachkompetenz Wissen (professional expertise):
    Studierende...
    Students shall...
  • discuss the emerging technological issues facing managers (Factual and Methodic Competence). explain the value of data, information, and knowledge to organizations (Factual and Methodic Competence).
  • design and develop Database Management Systems, Management Information Systems, Decision Support Systems, Strategic Information Systems, and Expert System in support of the organizational decision making and problem solving (Methodic and Transfer Competence).

  • Fachkompetenz Fertigkeit (practical professional and academic skills):
    Studierende...
    Students shall...
  • utilize information technology tools to design operational, managerial, and strategic systems. 
  • utilize a series of decision analytics tools in a hands-on environment (Methodic and Transfer Competence).
  • Relational Database Management Modeling
  • Mathematical Optimization
  • Natural Language Programming
  • Influence Diagramming
  • Multi-Criteria Decision Analysis
  • Decision Tables
  • Decision Trees
  • Structured English
  • Knowledge Engineering
  • Rule-Based Expert Systems

  • Personale Kompetenz / Sozial (individual competences / social skills):
    Studierende...
    Personale Kompetenz / Selbstständigkeit (individual competences / ability to perform autonomously):
    Studierende...
    Students shall...
  • discuss when and how Management Support Systems may be used to complement more analytic decision-making frameworks (Factual and Transfer Competence).
  • demonstrate ability to collaborate within a diverse group of people and make complex decisions (Normative and Transfer competence).

  • Prüfungsleistungen (examinations)
    Art der Modulprüfung (type of modul examination): Modulabschlussprüfung
    Art der Prüfung
    (type of examination)
    Umfang
    (extent)
    Gewichtung
    (weighting)
    a) Klausur 60 minutes - A hands-on and open-book exam in the computer classroom including five problems on Database Management Systems, Decision Support Systems, and Expert Systems. 60.00 %
    b) Projektarbeit 30 minutes - A multi-disciplinary group project designed to solve a complex real-life strategic information problem using Strategic Assessment Systems and the Euclidean Model. 40.00 %
    Studienleistung / qualifizierte Teilnahme (module participation requirements)
    Nein
    Voraussetzungen für die Teilnahme an Prüfungen (formal requirements for participating in examinations)
    Keine
    Voraussetzungen für die Vergabe von Credits (formal requirements for granting credit points)
    Die Vergabe der Credits erfolgt, wenn die Modulnote mindestens „ausreichend“ ist
    Gewichtung für Gesamtnote (calculation of overall grade)
    Das Modul wird mit der doppelten Anzahl seiner Credits gewichtet (Faktor: 2)
    Verwendung des Moduls in den Studiengängen (The module can be selected in the following degree programmes)
    B.Sc. International Business Studies, B.Sc. Wirtschaftsinformatik, B.Sc. Wirtschaftswissenschaften
    Umfang QT (participation requirements):
    Lernmaterialien, Literaturangaben (learning material, literature):
    Sonstige Hinweise (additional information):

    Dieser Kurs wird von dem Gastdozenten und Honorarprofessor der Universität Paderborn, Herrn Prof. Dr. Madjid Tavana von der La Salle University in Philadelphia gehalten und findet üblicherweise jährlich im Sommersemester in Form eines Blockseminars im Mai statt. Die Kurssprache ist Englisch.​


    This course is held by the guest lecturer and honorary professor of Paderborn University, Prof. Dr. Madjid Tavana from La Salle University in Philadelphia and usually takes place annually in the summer semester as a block seminar in May. ​The course language is English.


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