hybrid
Applied Data Science: Machine Learning for Economics and Business Data
Provided by: UMA
(EQF level: 8)
This course provides a gentle introductory and practical approach to understanding and analyzing data using machine learning and artificial intelligence techniques with applications for economics and business data.
The course will be held in a hybrid format on Tuesdays from 15:30 to 17:00.
Lecturer:
Prof. Dr. Davud Rostam-Afschar is a professor at the University of Mannheim and Academic Director of the German Business Panel. He has previously held visiting research positions at UC Berkeley and Harvard University, and has conducted research and taught at the University of Hohenheim, the Free University of Berlin and the University of Potsdam. He is a consultant to the European Commission and the OECD. Besides accounting and taxation, his research focuses on macroeconomics, public economics, labor economics, and industrial economics, in particular the analysis of corporate taxation and regulation. He has also a research interest in econometrics and survey methods.
Assessment: Paper / technical report (50 %), Presentation (25 %), Class participation (25 %)
The course will be held in a hybrid format on Tuesdays from 15:30 to 17:00.
Lecturer:
Prof. Dr. Davud Rostam-Afschar is a professor at the University of Mannheim and Academic Director of the German Business Panel. He has previously held visiting research positions at UC Berkeley and Harvard University, and has conducted research and taught at the University of Hohenheim, the Free University of Berlin and the University of Potsdam. He is a consultant to the European Commission and the OECD. Besides accounting and taxation, his research focuses on macroeconomics, public economics, labor economics, and industrial economics, in particular the analysis of corporate taxation and regulation. He has also a research interest in econometrics and survey methods.
Assessment: Paper / technical report (50 %), Presentation (25 %), Class participation (25 %)
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Fall 2026
Course start date 2026-09-08Course end date 2026-12-08Language EnglishCredits 5 (ECTS)Grading scheme: pass/fail