*This course is an introduction to*

*causal data science with directed acyclic graphs*(DAG). DAGs combine mathematical graph theory wit Hot & New
105 students enrolled

Created by
Paul HÃ¼nermund

What you'll learn

- Causal inference in data science and machine learning
- How to work with directed acylic graphs (DAG)
- Newest developments in causal AI

Requirements

- Basic knowledge of probability and statistcs
- Basic programming skills would be an advantage

#### Description

The course provides a good overview of the theoretical advances that have been made in causal data science during the last thirty year. The focus lies on practical applications of the theory and students will be put into the position to apply causal data science methods in their own work. Hands-on examples, discussed in the statistical software package R, will guide through the presented material. There are no particular prerequisites for participating. However, a good working knowledge in probability and basic programming skills are a benefit.

## 0 Comments