| Subject |
Basic concepts of statistics: estimation, testing, prediction and classification, clustering
basic statistical tools: frequentist vs. Bayesian inference; common statistical models; model selection and model averaging
big data and large scale inference: big "n" vs. big "p"; sparse modelling and Lasso; Random forests, boosting, shrinkage and empirical Bayes;
pitfalls: correlation vs. causation; all models are wrong; garbage in - garbage out; common sources of bias; Simpson's paradoxy and the perils of aggregating data; data mining, multiple hypothesis testing and the false discovery rate ; curse of dimensionality, spurious correlation,
incidental endogeneity
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