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

Class notes (Google Drive) Theory: Salvati L. et al. (2025). Analisi dei dati. CISU, Roma (seconda edizione). Exercises and examples: Anzalone F.M - Maialetti M. - Salvati L. (2024). I territori del PNRR. Applicazioni economiche con indicatori statistici CISU, Roma. Reading of a third book is compulsory for remote students: Salvati L. (2024). Statistica, economia e sostenibilità. Indicatori per l'analisi regionale. Franco Angeli, Milano. Free softwares for exercises.

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.