About the Course

Statistical Methods for Data Representation and Classification

One of the most important aim in statistical analysis is the reduction of complexity to make patterns in data visible. This course will cover some of the most widely used statistical methods for classification of observations and finding groups in data. The methods are illustrated with examples from the social sciences and the application of the methods in the statistical programming language R is addressed.

Topics
• Objectives of classification methods and quality criteria for classifications
• Distance measures
• Multidimensional Scaling (MDS)
• Hierarchical and non-hierarchical cluster analysis methods
• Model-based classification methods (latent class analysis)
• Classification trees and machine learning
• Validation of classification solutions

Part of the seminar is an introduction into classification methods with "R" (www.r project.org). R is a free open source software for data analysis, which is widely used in academic research and business applications.

Requirements for course certification / module examinations
Attendance certificates can be acquired through active participation and the completion of exercises. The module examination additionally includes the preparation of a term paper.

Lehrende

Sebastian Jeworutzki

Termine

• Monday, 10.10.2022 (1. Termin)
  14:00 bis 16:00 Uhr
  GD E2/208 CIP-Pool

Anmeldung

Bitte melden Sie sich in eCampus für die Veranstaltung an.

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