Skip to main content
TR EN
DA 519 Causal Data Science
Causal data science has recently become a sub-discipline of general data science. The aim of this area is to draw cause-effect relationships from experimental and especially observational data. With this, the possible effects of the plannned interventions will be better understood. Application areas of causal data science consist of medicine, economy and finance, marketing, political sciences, management and tech industry. The main output of this course will be that the students will be able to obtain cause-effect relationships with modern machine learning methods. The course will be taught with applications in Python.
SU Credits : 3.000
ECTS Credit : 6.000
Prerequisite : -
Corequisite : -