Vorlesung + Übung: Financial Data Analytics - Details

Vorlesung + Übung: Financial Data Analytics - Details

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General information

Course name Vorlesung + Übung: Financial Data Analytics
Course number WIW-5283
Semester WS 2024/25
Current number of participants 34
Home institute Prof. Dr. Jan Muntermann - Financial Data Analytics
Courses type Vorlesung + Übung in category Teaching
Next date Tuesday, 14.01.2025 14:00 - 15:30, Room: (K 1003)
Performance record Written Exam
Veranstaltung findet in Präsenz statt / hat Präsenz-Bestandteile Yes
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 points 6

Rooms and times

(K 1003)
Tuesday: 14:00 - 15:30, weekly (13x)
(CIP 2113)
Wednesday: 12:15 - 13:45, weekly (11x)

Module assignments

Comment/Description

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.

Admission settings

The course is part of admission "Anmeldung mit Passwort: Financial Data Analytics".
The following rules apply for the admission:
  • Password required.