Management of technologies, innovation and sustainability

1. General intro. 2. Operational Data mining. 3. Data base theory and big data. 4. Operational principles of multivariate statistics and data interpretation; Software for multivariate statistics. 5. Factor analysis and principal component analysis. 6. Cluster analysis. 7. Metric and non-metric multi-dimensional scaling (MDS). 8. Correspondence analysis and Canonical correlation analysis (CCA). 9. Regression analysis.

Linear algebra; Descriptive and inferential statistics

Slides in Google Drive Theory: Maialetti M. - Sateriano A. (2024). Analisi esplorativa dei dati. CISU, Roma (ultima edizione). Exercises/applications: Maialetti M. - Salvati L. (2024). Sostenibilità e resilienza. Analisi quantitativa e applicazioni economiche. Franco Angeli, Milano (ultima edizione). A third book is adopted for remote students: Orlandi V. - Maialetti M. - Salvati L. (2024). Indicatori territoriali e sviluppo locale. Verso un'economia del paesaggio. Carocci, Roma. Free software

Class lesson. Laboratory with software.

Class frequency

Written and oral; partial evaluations are possible during the class term; lab/project works allowed alone or in team

The same of above

Class lesson. Laboratory with software.