Management of technologies, innovation and sustainability

LUCA SALVATI

To prepare students for the knowledge and routine use of databases and the main data analysis techniques for business and professional practice, and for sustainability analysis. To provide professional tools, including advanced ones, to develop multivariate statistical analyses in economics. To clarify operational procedures for independently reading statistical results, interpreting them, and writing research reports and documentation based on the same data collected and analyzed previously, including marketing and customer satisfaction surveys. To introduce the advanced use of graphical, tabular, and map tools.

To run exploratory statistics and data analysis in multivariate fields. To read the results of such analyses and write reports and other documents using the appropriate terms. To contextualize problems and use multivariate statistics to give a response to them

Linear algebra; Descriptive and inferential statistics

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

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.

The same of above

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

To provide an accurate description, written and oral, of the outcomes of a multivariate statistical analysis run at class