Allgemeine Informationen

Veranstaltungsname	Vorlesung + Übung: Financial Data Analytics
Veranstaltungsnummer	WIW-5283
Semester	WS 2025/26
Aktuelle Anzahl der Teilnehmenden	29
Heimat-Einrichtung	Prof. Dr. Jan Muntermann - Financial Data Analytics
Veranstaltungstyp	Vorlesung + Übung in der Kategorie Lehre
Nächster Termin	Dienstag, 13.01.2026 14:00 - 15:30, Ort: (K: 1003)
Leistungsnachweis	Written Exam
Veranstaltung findet in Präsenz statt / hat Präsenz-Bestandteile	Ja
Hauptunterrichtssprache	englisch
Literaturhinweise	M. Alvarez (2007) Market Data Explained: A Practical Guide to Global Capital Markets Information, Oxford, Elsevier Sharda, R.; Delen, D.; Turban, E. (2020) Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support, 11th Ed., Prentice Hall, NJ. Sabherwal, R.; Becerra-Fernandez, I. (2013) Business Intelligence: Practices, technologies and management, John Wiley & Sons, NY. Tan, P.; Steinbach, M.; Karpatne, A.; Kumar, V. (2018) Introduction to Data Mining, 2nd Ed., Addison Wesley, Boston. Han, J.; Pei, J.; Tong, H. (2022) Data Mining: Concepts and Techniques, 4th Ed., Morgan Kaufmann, Waltham, MA.
ECTS-Punkte	6

Lehrende

Räume und Zeiten

(K: 1003): Dienstag: 14:00 - 15:30, wöchentlich (12x)
(CIP 2113): Mittwoch: 12:15 - 13:45, wöchentlich (9x)

Studienbereiche

Wirtschaftswissenschaftliche Fakultät > Master WiWi

Modulzuordnungen

Kommentar/Beschreibung

Subject-related Competencies :
Upon successful completion of this module, students will have expertise in the key methodological aspects of advanced data analysis in financial contexts. They will be able to differentiate between the most relevant data structures, models and standards in finance. They will also be able to apply the basic principles of descriptive, predictive and prescriptive analytical approaches to support financial decision making.

Methodological Competencies :
Based on the introduced methodological foundations, students will be able to apply and evaluate different methodological approaches for advanced data analytics, in particular data and text mining techniques, to support decision-making in various financial contexts.

Interdisciplinary Competencies :
The expertise gained in this course allows students to bridge methodological knowledge with practical applications in financial decision-making, thereby acquiring interdisciplinary competencies.

Key Competencies:
Students demonstrate critical thinking and problem-solving skills in addressing complex decision situations. They will be able to apply advanced data analytics techniques to real-world challenges.

Vorlesung + Übung: Financial Data Analytics - Details