Data Analytics & 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

• American Psychological Association (Washington, District of Columbia) (Ed.). (2020). Publication manual of the American psychological association (Seventh edition). American Psychological Association.
• Coren, E., & Wang, H. (Eds.). (2024). Storytelling to Accelerate Climate Solutions. Springer International Publishing. https://doi.org/10.1007/978-3-031-54790-4
• De Wolf, C., Çetin, S., & Bocken, N. M. P. (Eds.). (2024). A Circular Built Environment in the Digital Age. Springer International Publishing. https://doi.org/10.1007/978-3-031-39675-5
• Garg, V., Goel, R., Tiwari, P., & Döngül, E. S. (2024). Handbook of Artificial Intelligence Applications for Industrial Sustainability: Concepts and Practical Examples (1st ed.). CRC Press. https://doi.org/10.1201/9781003348351
• Heath, C., & Starr, K. (2022). Making numbers count: The art and science of communicating numbers (First Avid Reader Press hardcover edition). Avid Reader Press.
• Jamieson, K. H., Kahan, D., & Scheufele, D. A. (2017). The Oxford handbook of the science of science communication. Oxford university press.
• Montgomery, S. L. (2017). The Chicago guide to communicating science (2nd ed). University of Chicago press.
• Viceconti, M., & Emili, L. (Eds.). (2024). Toward Good Simulation Practice: Best Practices for the Use of Computational Modelling and Simulation in the Regulatory Process of Biomedical Products. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-48284-7

Portfolio

English

5

50

2.5

Blended Learning

2

director of studies

DEM

integrated lecture

Compulsory

none