40720 - Data Mining

Learning outcomes

This course introduces students to the study of the main statistical data mining methods to extract useful information from huge databases and to support the business intelligence process with explorative and predictive analysis.

Expected learning outcomes: at the end of the course the student is able to select the most appropriate methodology to the decision process problem, to quantitatively analyse the relationship between business phenomena and to critically interpret empirical results.

Course contents

1. Introduction to data mining.

2. Organization of data: data objects and attributes type, data matrices and their transformations.

3. Data Preprocessing and Exploratory Analysis: data cleaning, data bivariate exploratory analysis of qualitative and quantitative data.

4. Measures of Distance.

5. Hierarchical and non-hierarchical Cluster Analysis.

6. Classification and prediction methods: logistic regression. 

Readings/Bibliography

• Stéphane Tufféry. Data Mining and Statistics for Decision Making. 2011. John Wiley & Sons.

• Giudici, P. , Figini, S. Applied Data Mining. 2009. John Wiley & Sons.

Teaching methods

The module consists in theoretical session on methods and practical tutorials devoted to applications on real economic data, through the use of SAS statistical software.

Assessment methods

Written exam consisting in a multiple-choice section and a section requiring production and interpretation of statistical outputs. The multiple choice section aims at testing the student's knowledge of the theoretical topics. The second section is targeted at testing the ability of producing and interpreting statistical outputs, and their translation into applied conclusions.

Teaching tools

SAS software demonstrations on data analyisis will be provided. Notes are downloadable from the lecturer's web page.

Office hours

See the website of Ida D'Attoma