Basic statistical tools: frequentist vs. Bayesian inference; common statistical models; model selection and model averaging.

Pitfalls: correlation vs. causation; all models are wrong; garbage in - garbage out; common sources of bias; Simpson's paradox and the perils of aggregating data; data mining, multiple hypothesis testing and the false discovery rate; curse of dimensionality, spurious correlation, incidental endogeneity.

Big data and large scale inference: big "n" vs. big "p"; introduction to and application of a specific advanced statistical method such as, for example, sparse modelling and lasso; random forests, boosting, shrinkage and empirical Bayes.