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98058 - Describing phenomena and controlling uncertainty

Academic Year 2021/2022

                
                        Docente:
                        Maria Ferrante
                    
                        Credits:
                        6
                    
                        SSD:
                        SECS-S/01
                    
                        Language:
                        Italian
                    
                        Moduli:
                        
                            Maria Ferrante
                            (Modulo 1)
                        
                            Luca Trapin
                            (Modulo 2)
                        
                            Stefania Mignani
                            (Modulo 3)
                        
                        Teaching Mode:
                        
                                    Traditional lectures (Modulo 1)
                                
                                    Traditional lectures (Modulo 2)
                                
                                    Traditional lectures (Modulo 3)
                                
                            Corso:
                             Minor "Learning from data"
                        
                            Teaching resources on Virtuale

Learning outcomes

At the end of the course the student will be able to read, analyze and communicate with data. In particular he/she will know how to interpret frequencies, mean values, variability measures and graphical representations; he/she will reason in probabilistic terms and will be able to recognize the pitfalls and the advantages connected with the use of sample data. He/she will have acquired the theoretical and empirical knowledge required to distinguish between association and causation and he/she will know how to build and interpret simple methods of statistical learning in the context of regression and classification.

Course contents

PART 1: Poverty and inequality measures

Absolute and relative poverty
Incidence and intensity of poverty
Deprivation
Inequality
Sampling surveys and administrative data

PART 2: Time series analysis

Cross-sectional and time series data
Graphical tools for time series
Price indices and the GDP
Growth rates
Moving averages

PART 3: Data communication

Data classification: From distributions to cluster analysis
Appropriately analyze and interpret relationships among variables
Challenges of data visualization: avoiding mistakes

Readings/Bibliography

David Spiegelhalter, The art of statistics: How to learn from data, Pelican Books Ltd., 2020
Alberto Cairo, Come i grafici mentono. Capire meglio le informazioni visive, Raffaello Cortina Editore, 2020

Teaching methods

Frontal lessons and discussion of case studies.

Assessment methods

The aim of the assessment is to evaluate the analytical skills gained by the student on the specific topics discussed during the course.

The assessment requires the student to discuss and interpret the results of a statistical analysis on a case study similar to those presented in class. As the course is divided in three modules, students will be randomly split in three groups and each group randomly assigned to one of the modules. Students will be then evaluated on a case study related to the module of the group they belong.

Teaching tools

Teaching material provided by the instructor; suggested textbooks; online material; oper-source datasets.

Office hours

See the website of Maria Ferrante

See the website of Luca Trapin

See the website of Stefania Mignani