Data Science and Artificial Intelligence

Special Methods for Data Analysis

Integrated course, 5.00 ECTS

 

Course content

The course introduces multivariate statistical methods in detail. These include topics such as correlation and covariance, partial least squares, PC analysis, discriminant analysis, factor analysis, Markov chains and their application, maximum likelihood methods and Bayesian statistics.

Learning outcomes

The graduate gains mathematical-statistical skills that are required for practical data analysis.

Recommended or required reading and other learning resources / tools

Books: Simon Munzert: Automated Data Collection with R; Udo Kuckartz, Statistik; Andy Field: Discovering Statistics Using SPSS; Wolf-Michael Kähler: Statistische Datenanalyse
Journals:

Mode of delivery

2 THW Lecture, 1 THW Tutorial

Prerequisites and co-requisites

Module MAT 1

Assessment methods and criteria

Lecture: final exam, Tutorial: continuous appraisal