wir891 - Complex Data Analysis (Complete module description)

wir891 - Complex Data Analysis (Complete module description)

Original version English PDF Download
Module label Complex Data Analysis
Modulkürzel wir891
Credit points 6.0 KP
Workload 180 h
Institute directory Department of Business Administration, Economics and Law (Business Administration and Business Education)
Verwendbarkeit des Moduls
  • Master Applied Economics and Data Science (Master) > Empirical Methods
  • Master's programme Business Administration: Management and Law (Master) > Schwerpunktmodule AFT - Methoden
  • Master's programme Business Administration: Management and Law (Master) > Schwerpunktmodule NM - Methoden
  • Master's programme Business Administration: Management and Law (Master) > Schwerpunktmodule UF - Methoden
  • Master's Programme Sustainability Economics and Management (Master) > Additional Modules
Zuständige Personen
  • Stecking, Ralf Werner (module responsibility)
  • Lehrenden, Die im Modul (Prüfungsberechtigt)
Prerequisites
Skills to be acquired in this module
With successful completion of the course, students shall be able to analyze complex empirical data sets, like aggregated data, privacy constrained data, distance information, distributions, tables, symbolic or granular data. Students will also learn to handle issues of big data challenges: large number of cases or variables, unknown dependencies, redundancy, missing values, small or no variance. In this course students will learn theoretical aspects of complex data analysis, as well as practical applications for real data sets with statistical software packages.
Module contents

Principal Component Analysis, Correspondence Analysis, Cluster Analysis, Linear Discriminant Analysis, Multidimensional Scaling, CART, Symbolic Data Analysis
Literaturempfehlungen

Billard, L. and Diday, E. (2006): Symbolic Data Analysis, West Sussex

Hastie, T., Tibshirani, R. and Friedman, J. (2001): The Elements of Statistical Learning, New York

Pedrycz, W. (2017): Granular Computing, Boca Raton

Tuffery, S. (2011): Data Mining and Statistics for Decision Making, West Sussex
Links
Languages of instruction German, English
Duration (semesters) 1 Semester
Module frequency
Module capacity unlimited
Lehrveranstaltungsform Comment SWS Frequency Workload of compulsory attendance
Lecture 2 SoSe oder WiSe 28
Seminar 2 SoSe oder WiSe 28
Präsenzzeit Modul insgesamt 56 h
Examination Prüfungszeiten Type of examination
Final exam of module
Am Ende der Vorlesungszeit
Klausur oder Mündliche Prüfung oder Hausarbeit oder Referat