wir891 Complex Data Analysis (Vollständige Modulbeschreibung)

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.

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

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

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