This seminar is taught in English.
We recommend successful completion of the courses "Einführung in die Statistik" and "Einführung in die Ökonometrie".
Regelmäßige Seminarteilnahme; 2x R-Markdown Skript (ausführlich kommentierter R-Code inkl. Tabellen, Graphen), einmal als Zwischenabgabe (ohne Benotung) und einmal als benotete Prüfungsleistung und Poster Präsentation (zwischen 20 und 30 Minuten inkl. Fragen).
Portfolioprüfung: 6 ECTS
This applied seminar has two main objectives: first, to provide students with practical skills in econometrics and data science, with a focus on using R. Students will learn how to manage data comprehensively, from data cleaning and wrangling to automating tasks for greater efficiency. Through practical sessions, they will be guided in conducting exploratory data analysis and creating visualizations, which are crucial for discovering patterns and insights in data.In the second part of the course, students will be introduced to both unsupervised and supervised machine learning techniques, essential tools in contemporary econometric analysis. They will gain hands-on experience applying these methods to real-world data, learning how to integrate these techniques within economic contexts.Throughout the semester, students will work on projects in small groups, with opportunities to present their progress during the course. At the end of the semester, the students will turn in their R Code and present their results in a poster presentation.
