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Nr. LV-Typ(en) LV-Titel
5700 PI Advanced Research Methods & Project Management (Group D) Präsenz-Modus
Anmeldung durch das Institut
vom 12.02.2024 14:00 bis 29.02.2024 23:59
Abmeldung durch das Institut
vom 12.02.2024 14:00 bis 05.03.2024 23:59

LV-Leiter/in o.Univ.Prof. Dr. Alfred Taudes, Martin Hrusovsky, Ph.D., Dr. Martin Waitz, ao.Univ.Prof. Dr. Alexander Prosser
Planpunkte Master Advanced Research Methods & Project Management
Semesterstunden 1
Unterrichtssprache Englisch

Termine
Fr, 08.03.2024 09:00-12:30 Uhr D2.0.031 Workstation-Raum (Lageplan)
Fr, 15.03.2024 09:00-12:30 Uhr TC.-1.61 (Lageplan)
Do, 16.05.2024 13:00-16:30 Uhr TC.3.02 (Lageplan)
Do, 06.06.2024 13:00-16:30 Uhr TC.3.02 (Lageplan)
Termindownload (ical) | Termine abonnieren

Weitere Informationen https://learn.wu.ac.at/vvz/24s/5700

Kontakt:
martin.hrusovsky@wu.ac.at
Inhalte der LV:

Following up on the contents of the classes on “Academic Writing” in the first semester, this class delves deeper into academic research methods. Specific methods and their applications are explained in detail, i.e.

  • Advanced process modelling
  • Big data and machine learning
  • Structured literature review
  • (Agent-based) simulation

 
Lernergebnisse (Learning Outcomes):

After completion of this course, students have a basic understanding of how research in Supply Chain Management is conducted. They are able to formulate a research question and recognize the existence of different research methods. Furthermore, students are able to differentiate between the different research methods and can decide which research methods is applicable in certain research situations.
Regelung zur Anwesenheit:

Attendance in all sessions (full presence) is mandatory.
Lehr-/Lerndesign:

Lecture, classroom discussions, assignments, cases
Leistung(en) für eine Beurteilung:

Assessment will be based on 4 assignments, including 1 in-class assignment and 3 homework assignments. Each assignment has equal weight of 25%.

Grading scale:

  • Excellent (1): 90.0% - 100.0%
  • Good (2): 80.0% - <90.0%
  • Satisfactory (3): 70.0% - <80.0%
  • Sufficient (4): 60.0% - <70.0%
  • Fail (5): <60.0%

 
Zuletzt bearbeitet: 23.11.2023 13:38
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