Data Science for Socioeconomists 2.0

• Course type: Lecture and exercise
• Organisation: Faculty of Business, Economics and Social Sciences
• Funding period: 01.09.2024 to 31.07.2025
• Short title: DDSOEC2

Orientation of the project

The aim of the project was to introduce social economics students to data science. In a hybrid course, they learned theoretical and practical knowledge about regression and classification methods. The lecture was supplemented with code examples, providing hands-on knowledge. In the exercise, the acquired knowledge was applied in practice through small exercises. The students worked collaboratively in small groups with JupyterHub and documented their results in Quarto, learning project documentation and acquiring knowledge via pair programming.

Two instructors prepared the material and gave an introduction. They also supported the students during the group work phase, encouraging them to help each other and solve problems together. At the end of each exercise session, various approaches to solving problems were discussed in a plenary session.

The objectives of the course were to reduce uncertainty in dealing with data and to teach programming with R using subject-specific examples. After completing the course, students were able to independently perform data analyses for their own research projects. In addition, the course prepared students for collaborative work and encouraged joint problem solving.

^{Scott Graham / unsplash}

Review and results

Students learned how to create graphics in a practical way. In addition, collaborative work enabled them to learn project work and project documentation skills. Furthermore, group work made it possible to positively leverage the students' highly heterogeneous prior knowledge. The use of code in the lecture and group work in the exercise proved to be successful. In addition, the students emphasized in their feedback that the course was very application-oriented and prepared them for future fields of work in economics, sociology, and business administration. The integration of two practical lectures—one from the field of work of an NGO and one from the field of IT consulting—given by former students of the Department of Social Economics was perceived as very enriching and gave the students a very good orientation to the field of work.

Tips from lecturers for lecturers

The lecture led to increased use of Quarto (slides) with the integration of code snippets and experience with using Quarto as a teaching tool. Quarto was also used in the exercise. However, in the second round, Quarto documents were used instead of slides, as these did not have any overflow issues and longer examples could be presented coherently instead of spread across several slides. The teaching concept of small group work allowed didactic skills to be developed in supporting small groups. The new, face-to-face setting for this course format made it possible to be closer to the participants and to identify problems more easily. At the same time, however, it also required more motivation and frustration management on the part of the teachers.

Persons involved

Prof. Dr. Ulrich Fritsche

Professor of Economics, in particular Empirical Economic Research

• Scientific specialisations: Applied macroeconomic research, especially analysis of forecasts and expectations, expectation formation in macroeconomic models and computational text analysis
• Teaching specialisations: Methods of empirical economic and social research, macroeconomics and history of theory

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Lisa Marie Wegner, M. A.

Research assistant

• PhD student with research focus: economic change; narrative economics
• Teaching: Macroeconomics, basic economics course and Data Science for Socio-Economists

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Junbo Huang, M.Sc.

Research assistant

• PhD student with research focus: Computational Narrative Understanding, Natural Language Processing, Machine Learning
• Teaching specialisations: Text as Data, Data Science

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Victoria Hünewaldt, M. Sc.

Research assistant

• PhD student with research focus: Perceptions of inequality and distributional preferences; Narrative Economics
• Teaching: Econometrics & Data Science for Socio-Economists

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