The course provides the student with an introduction to advanced econometrics. The subjects include, probability, asymptotic theory, time series, volatility models and simulation methods in econometrics.

The course is intended for master students in finance and economics with an interest in statistics and econometrics. The course can also be taken by doctoral students.

Enrollment key: ATSE2019.

Data mining is the process of exploring large amounts of data to discover meaningful patterns or rules, a process becoming more and more important with the rapid growth of available information. The aim of the course is to provide an introduction to data mining techniques, focusing both on theory and practical applications. The course covers common methods for classification, prediction, association and clustering. We will look at methods such as Classification and Regression Trees, k-Nearest-Neighbors, Naïve Bayes Classifiers, Hierarchical and Non-hierarchical Clustering. The statistical program SPSS is used in the course.

The course alternates and is given every other year (see Additional information). 

Introduction to Data Mining  2019.pdfIntroduction to Data Mining 2019.pdf