Data analysis and Empirical Methods

Introduction and consolidation

The students are able to: • understand connections between research practice and fact-based decision-making processes in professional practice • understand the role of basic theoretical assumptions and concepts in the research process and research design • assess the strengths and applications of qualitative and quantitative methods for empirical research and to apply them in an exemplary manner • independently collect data sets with empirical methods • independently structure data sets, to analyze, present and critically evaluate information • select and implement methods of data analysis in the context of a specific problem • understand and apply concepts and methods of descriptive and explorative statistics as well as predictive data analysis • understand special requirements for data preparation and data storage • present and critically evaluate information

academic work and empirical methods at Bachelor level

Empirical methods and academic methods • research practice and fact-based decisions • qualitative and quantitative methods, research design and forms of data collection (e.g. interview, questionnaire, observation, field and laboratory study, experiment, simulation) • basics Exposé for the Master thesis Data Analysis • univariate and multivariate data analysis • predictive statistical data analysis (Machine Learning) and methodology of inferential statistics • probability theory, information theory, Bayes Theorem • system dynamics and agenda-based modeling • application of methods of data analysis • presentation and visualization of data

• James, G., Witten, D., Hastie, T., Tibshirani, R. 2013. An Introduction to Statistical Learning with Applications in R. Springer. New York. • Chakrabarti, A., L. Pichl und T. Kaizoji (Hrsg), 2019. Network Theory and Agent-Based Modeling in Economics and Finance. Singapur: Springer Nature • Stocker, H. 2014. Ökonometrie: Grundlagen und Methoden. Pearson Studium - Economic VWL • Fahrmeir, L., R. Künstler, I. Pigeot, I. und G. Tutz, 2012. Statistik: Der Weg zur Datenanalyse. 7. Auflage. Berlin: Springer • Fahrmeir, L., Kneib, T. & Lang, S., 2009. Regression: Modelle, Methoden und Anwendungen. 2. Auflage. Berlin: Springer • Heisen, M. R., Theisen, M., 2017. Wissenschaftliches Arbeiten: erfolgreich bei Bachelor- und Masterarbeit. München: Franz Vahlen

Portfolio

German

5

50

2.5

Blended Learning

2

director of studies

DEM

integrated lecture

Compulsory

none