Workshops
We offer tailor-made workshops in statistics, econometrics, and data science. The workshops can be held face to face in Dublin, elsewhere or online, in English or German. Our courses cater to practitioners at all levels, ranging from participants with little to no background in statistics to experts who want to learn about the most recent methodological advances. At any level, we explain the theory behind each method in an intuitive manner and place great focus on applications.
1. Data Analysis with R
R is a statistical programming language that enables its users to do pretty much everything: clean data, create descriptive statistics, produce nice-looking graphs, perform statistical analyses from simple linear models to complex spatial statistics or epidemiological models. One can even use R to produce nice-looking websites, presentations, and documents.
R has many advantages: it is free, has a large community and excellent AI support, and has many useful applications. R has a bit of a learning curve, but our experienced team will teach you the basics and even some advanced applications fairly quickly.
R Workshop I: Basic R Skills
- Basics of working with R and RStudio (set-up, workflow)
- Basic data management with R (loading data, cleaning data)
- Descriptive statistics
- Producing nice-looking graphs
- Linear regression
- etc
R Workshop II: Advanced Applications in R
- Data wrangling with dplyr and other tidyverse packages
- Plotting with ggplot
- Monte Carlo simulations
- Statistical inference
- Basic machine learning: regularisation, random forests, etc
- Producing documents, websites and presentations with R markdown or Quarto
- More advanced techniques such as web scraping, forecasting and big data analytics available on request
2. Causal Inference
Causal Inference is a highly useful statistical toolbox that allows users to credibly answer causal questions without running actual experiments. As much as randomised experiments are considered the gold standard of establishing causality, in many settings we cannot run experiments for practical or ethical reasons. Causal inference is particularly useful for policy evaluation in firms and organisations. Please see here for our Causal Inference course webpage with comprehensive materials and resources.
We offer causal inference workshops at all levels, from beginners to experts. We cover the following topics:
- Foundations of causality: DAGs and potential outcomes
- Selection on observables (regression, matching, etc)
- Randomised experiments
- Instrumental variables
- Difference-in-differences
- Synthetic controls
Moreover, we cover advanced topics such as:
- Bunching designs
- Marginal treatment effects
- Causal machine learning
- Bounding
A sample of our teaching on Causal Inference can be found on this YouTube playlist:
3. Econometrics & Data Science for Practitioners
We can cover a wide range of topics in econometrics, statistics, and data science. For more information, please contact us.