Data analysis and Empirical Methods
Niveau
Introduction and consolidation
Learning outcomes of the courses/module
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
• 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
Prerequisites for the course
academic work and empirical methods at Bachelor level
Course content
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
• 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
Recommended specialist literature
• 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
• 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
Assessment methods and criteria
Portfolio
Language
German
Number of ECTS credits awarded
5
Semester hours per week
Planned teaching and learning method
Blended Learning
Semester/trimester in which the course/module is offered
2