Data Mining and Computational Statistics

This is an introductory course to basic techniques and applications in finance and economics of Data Mining and Computational Statistics, also in the more general framework of data science. We will allow students to develop programming skills using the R software. By the end of the course, students will be equipped to apply these methods to solve practical problems in the analysis of economic and financial data.

At the end of the course students will be able to perform machine learning techniques and algorithms and use them in economic and financial applications. Specifically, students will be familiar with supervised and unsupervised models. In particular, in the supervised framework students will be able to perform advanced regression models like the ridge and lasso regression, classification techniques like the Bayes classifier, the K-NN classifier and the logistic model, whereas in the unsupervised framework students will become familiar with dimensional reduction techniques and cluster analysis. More sophisticated techniques like decision tree-based classification will be presented to the students. In Computational statistics, resampling techniques, random number and random variable generation and numerical integration will be part of the acquired knowledge the students will have at the end of the course.