Learning objectives
The purpose is to deal in a quantitative way the relevant information for the firm. The data can come from different sources (internal or sample surveys) . The final goal is to provide a rational support for decision making.
Prerequisites
Basic knowledge of mathematics and statistics
Course unit content
Multivariate data analysis: data warehouse and data mining.
Exploratory data analysis: missing values and outliers
Introduction to SPSS and MATLAB.
Dimension reduction: principal component analysis. Applications to marketing problems.
Statistical methods for market segmentation: cluster analysis.
Full programme
Multivariate data analysis: data warehouse and data mining.
Exploratory data analysis: missing values and outliers
Introduction to SPSS and MATLAB.
Dimension reduction: principal component analysis. Applications to marketing problems.
Statistical methods for market segmentation: cluster analysis.
Bibliography
S. ZANI – A. CERIOLI, Analisi dei dati e Data Mining per le decisioni aziendali, Giuffrè Editore, Milano, 2007
Teaching methods
Frontal lessons also with PC
Assessment methods and criteria
Written exam
Other information
Additional information can be found from the web site http://www.riani.it
2030 agenda goals for sustainable development
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