KU.Campus

Detailed information about the module 
Module title:
Digital Seminar in Data Science & Quantitative Applications
Module title (english):
Digital Seminar in Data Science & Quantitative Applications
Module number:
82-021-D3B09-H-0124
Level:
Bachelor (UNI)
Course of study:
Type:
Modul
Organising faculty/Language Center:
Wirtschaftswissenschaftliche Fakultät
Instructors responsible:
Kuhn, Heinrich
Examiners:
Credit points (ECTS):
10
competencies/skills:
• Students will participate in the Pro-Seminar Supply Chain Management (SCM) (82-021-PS07-H-0507). Additionally to the requirements of the Pro-Seminar students will implement the corresponding data models, simulation models, or optimization models in a software system and carry through numeric analysis. The module will transmit the following competencies:
– Students will learn to work independently and scientifically on specific topics.
– Students will learn the methods of scientific working, for example literature research, citing, etc.
– Students will learn to implement data models, simulation models, or optimization models; as well as analyzing them with respect to economic problems.
– Students get used to basics of project management and teamwork.
– Students will learn to present a topic in a structured and comprehensible way in front of a group of experts.
– After having participated in this module students will have a basic knowledge of Supply Chain Management and will be able to provide transfer services in this context.
• In order to achieve these competencies we expect from the students an above average personal commitment as well as close coordination with the lecturer.
course content/topics:
• In the module “Digital Seminar in Data Science Quantitative Applications” students will work on different topics from Supply Chain Management.
• Basic knowledge of methods of quantitative analyzes and/or optimization will be transmitted.
• Methods of scientific working will be transmitted.
• Basics of project management, software development and teamwork will be transmitted.
formal requirements of admission:
none
recommended qualifications:
• Advanced studies, from 4th semester onwards.
• Basic knowledge of software development, specific software tools and/or programming languages with reference to data analysis.
Lehr- und Lernformen/Lehrveranstaltungstypen:
• Pro-Seminar
• Literature research and computational experiments
• Elaboration of a paper
• Presentation
requirements for the attainment of ECTS points:
• Performance record assessed with at least “sufficient” of portfolio.
• Due to the competencies, a combination of different examination modalities is necessary.
• The paper has to be elaborated with respect to the requirement of the faculty and should not exceed 20 pages.
• For the presentation, a time of 20 minutes is scheduled followed by 10 minutes of discussion. The presentations will be held in blocks of 3 topics; the students should additionally prepare a final discussion for the corresponding block concerning all the topics of this block.
• The proceeding while elaboration the paper as well as the teamwork within one of these blocks will be part of the grading.
workload/distribution of ECTS points within the module:
26 h = Time of attendance lecture
2 h = Introduction to scientific research
14 h = Preparation and follow-up seminar
78 h = Elaboration of the paper
30 h = Preparation of the presentation
150 h = Development, implementation, and analysis of the model
300 h = Total workload
calculation of module marks:
• Implementation of the software (50 %)
• Paper (30 %)
• Presentation (20 %)
teaching/learning method:
compatibility with other courses of study:
• Business Administration International B.Sc.
• Business Administration B.Sc.
• Data Science B.Sc.
Turnus des Angebots:
SS
Beteiligte Fachgebiete:
Bemerkung:
Teaching and examination language: • German or English. The language will be announced in advance. Literature: • Becker, Jörg; Kugeler, Martin; Rosemann, Michael (Hrsg.) (2012): Prozessmanagement. Ein Leitfaden zur prozessorientierten Organisationsgestaltung. 7. Edition, Berlin: Springer Berlin. • Feyerabend, P. (1986):Wider den Methodenzwang, 11. Edition, Suhrkamp (Frankfurt am Main). • Flynn, Barbara B.; Sakakibara, Sadao; Schroeder, Roger G.; Bates, Kimberly A.; Flynn, E. James (1990): Empirical research methods in operations management. In: Journal of Operations Management, Vol. 9, Iss. 2, 250–284. • Kuhn, Heinrich: Simulation, in: Köhler, R.; Küpper, H.-U. und A. Pfingsten (Hrsg.) (2007): Handwörterbuch der Betriebswirtschaft, 6. Edition, Stuttgart (Poeschel), Sp. 1624-1632. • Law, A.; Kelton, D.W. (2000): Simulation Modeling and Analysis. 3. Edition, New York: McGraw-Hill • Render, B.; R.M. Stair and M.E. Hanna (2017): Quantitative Analysis for Management, 13. Edition, Upper Saddle River: Prentice Hall. • Popper, Karl R. (2005): Logik der Forschung. 11. Edition, Tübingen: Mohr Siebeck. • Taha, H. (2022): Operations Research: An Introduction, 11. Edition, Upper Saddle River: Prentice-Hall.