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 is to familiarise students of social economics with data science. In the course, they will learn theoretical and practical knowledge about regression and classification methods. The lecture is supplemented by code examples so that "hands-on" knowledge is already imparted here. In the exercise, the acquired knowledge is applied in practice through small exercises. Students work collaboratively in small groups with JupyterHub and document their results in Quarto, learn project documentation and acquire knowledge via pair programming.

Two trainers prepare the material and give an introduction. They also support the students in the group work phase, encouraging them to help each other and solve problems together. At the end of a practice session, various solutions are discussed in plenary.

The aim of the course is to reduce uncertainties in dealing with data and to teach programming with R using specialised examples. After completing the course, students should be able to carry out data analyses for their own research projects independently. The course also prepares students for collaborative work and encourages them to solve problems together.

^{Scott Graham / unsplash}

Project realisation

The following key results were to be achieved: An introduction to the practical significance of data, statistical/machine learning methods and "big data" for economic and social science issues. This is followed by a basic introduction to programming with R, which enables students to independently create smaller research projects and understand the code of other researchers. They learn a range of data science methods, including data transformations, visualisation and simple methods of statistical learning, with a focus on supervised learning.

Basic ideas on in-sample/out-of-sample prediction and classification performance as well as the problem of dimension reduction (e.g. in lasso and ridge models or pruning of regression trees) are covered. Practical programming experience are taught using concrete examples from the social and economic sciences. Quarto presentations in the lecture explain the implementation of theoretical concepts with code snippets, which serve as the basis for the students' own analyses in the exercises.

The creation of graphics and the theoretical knowledge of design principles for data visualisations are learned both practically and theoretically, which is important for the use of empirical data in student research projects and theses. Students are also sensitised to the principles of open science and reproducibility. Two practical presentations by graduates of the Department of Socioeconomics, which are planned towards the end of the semester, are intended to provide insights into career development opportunities in the field of data science and contribute to career orientation.

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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