The course provides the basic knowledge for the analysis of big data flows originated by the digital revolution. The student is introduced to the sources in order to appreciate their complexity and informative capacity with the expected benefits for society, economy and public administration. The main methods of data acquisition, data analysis and spatial representation are then presented.
teacher profile teaching materials
Giuseppe Arbia, Statistica, nuovo empirismo e società nell'era dei Big Data, NUOVA CULTURA, 2018.
Gary Koop, Logica statistica dei dati economici. UTET Università, 2001 (Dal capitolo1 al capitolo 7)
Dispense del docente su piattaforma Moodle, sezione Materiali didattici.
Programme
Digitalization and statistical production. big data and informative bases for decision making. Descriptive analysis and methods for data flow analysis. Georeferenced data and representation in maps. Introduction to Text Data Mining: data preparation, extraction, and visualization.Core Documentation
Giovanni Azzone, Francesco Caio “In un mare di dati”, Mondadori, Milano, 2020.Giuseppe Arbia, Statistica, nuovo empirismo e società nell'era dei Big Data, NUOVA CULTURA, 2018.
Gary Koop, Logica statistica dei dati economici. UTET Università, 2001 (Dal capitolo1 al capitolo 7)
Dispense del docente su piattaforma Moodle, sezione Materiali didattici.
Reference Bibliography
Stefano M. Iacus, Guido Masarotto Laboratorio di statistica con R, McGraw-Hill Education, 2007 Giuseppe Espa, Rocco Micciolo, Problemi ed esperimenti di statistica con R, Apogeo Education, 2014Type of delivery of the course
Class lectures and seminars on the main topics.Attendance
The student who chooses to attend the lessons will have the opportunity to acquire quantitative tools in the classroom to facilitate the understanding of the course topics.Type of evaluation
The examination consists of a written paper (an empirical analysis to be agreed with the teacher) and oral test for both attending and non-attending students.