wir891 - Complex Data Analysis (Complete module description)

wir891 - Complex Data Analysis (Complete module description)

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Module label Complex Data Analysis
Module code wir891
Credit points 6.0 KP
Workload 180 h
Institute directory Department of Business Administration, Economics and Law (Business Administration and Business Education)
Applicability of the module
  • 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) > Supplementary Modules
Responsible persons
  • Stecking, Ralf Werner (module responsibility)
  • Lehrenden, Die im Modul (authorised to take exams)
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
Recommended reading

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
Type of course Comment SWS Frequency Workload of compulsory attendance
Lecture 2 SuSe or WiSe 28
Seminar 2 SuSe or WiSe 28
Total module attendance time 56 h
Examination Prüfungszeiten Type of examination
Final exam of module
Am Ende der Vorlesungszeit
Klausur oder Mündliche Prüfung oder Hausarbeit oder Referat