Teaching

🎒 Introduction to Data Science

The course is currently running and the materials are updated continuously.

Topics covered: Base R basics and best practice, Version control and project management with GitHub, Tidyverse, Functions, Iteration and Data bases, Web scraping with R, Modelling, Visualization, Working with the Shell, Debugging and Packaging, Interactive Web Applications with Shiny

Access all materials here!

🎒 Statistical Modeling & Causal Inference

I reviewed the lecture materials and hold R tutorials on the practical implementation of causal inference techniques for two diverse, interdisciplinary and international groups of students on a weekly basis. In collaboration with Sebastian Ramirez-Ruiz, we constructed weekly assignments to assess their learning progress continuously.

Topics covered: Causality, DAGs, the Potential Outcomes Framework, Regression, Matching, Instrumental Variables, Regression Discontinuity Designs, Difference in Difference, Panel Data and Fixed Effects, Moderation and Mediation.

If you are interested in learning and implementing causal inference techniques in R, check out our
open causal inference course materials!

🎒 Quantitative Methods II

I reviewed the lecture materials and hold tutorials on how to derive estimates by hand (conceptually) and in R (applied).

Topics covered: Analyses of Variance, Repeated Measures, (Semi)Partial Correlation, Multiple Regression, Analyses of Covariance, Non Parametrical Tests.

🎒 Quantitative Methods I

I reviewed the lecture materials and hold tutorials on how to derive estimates by hand (conceptually) and in R (applied).

Topics covered: Scales and Variables, Measurement Theory, Descriptive Statistics, Logic of Statistical Tests and Statistical Inference, T-Tests, Correlation and Regression.