You are here:

B5176 - Big Data Analytics

Academic Year 2026/2027

                
                        Docente:
                        Anna Gloria Billè
                    
                        Credits:
                        6
                    
                        SSD:
                        STAT-02/A
                    
                        Language:
                        Italian
                    
                        Teaching Mode:
                        In-person learning (entirely or partially)
                        
                            Campus:
                            Bologna
                        
                            Corso:
                            Second cycle degree programme (LM) in
                            Business Administration (cod. 6798)

                            Teaching resources on Virtuale
                        
                                    Course Timetable
                                
from Sep 16, 2026 to Oct 19, 2026

Learning outcomes

Al termine del corso, lo studente conosce i modelli statistici che sono alla base dell'attività di estrazione di conoscenza da grandi quantità di dati (Big Data). In particolare, lo studente è in grado di: - strutturare un processo di data mining; - scegliere, tra gli strumenti metodologici, quelli più adeguati a raggiungere l'obiettivo in esame; - interpretare criticamente i risultati.

Course contents

Introduction to data and Rstudio
Statistical Learning
Resampling methods: cross-validation
Linear models: OLS estimator, Gauss-Markov hypotheses and inference, interpretation, model selection. Nested/Non-nested models. Polinomial functions. Violation of the hypotheses: residual analysis and specification tests (heteroschedasticity, non-normality), robust OLS.
Model selection and Regularization: stepwise selection, ridge and lasso regression.
Classification: logistic regression
Time series: definition, residual analysis and specification tests (structural break and autocorrelation), robust OLS, time series components, forecasting with classical methods, statistical performance of forecasting methods.

Readings/Bibliography

Main Reference:

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani (2021), An Introduction to Statistical Learning with Applications in R, Springer.

Additional references:

Marno Verbeek (2005), Econometria, I edizione, Zanichelli
Editore.

Trevor Hastie, Robert Tibshirani, Jerome Friedman (2009), The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Second Edition).

Further references:

Tsai Chun-Wei et al. (2015), Big Data Analytics: a survey, Journal of Big Data, 2:21.

Teaching methods

Lectures are carried out considering both theoretical/methodological and empirical aspects in economics, with the help of the statistical software R.

The used economic datasets are all available in R or provided by the Professor.

Assessment methods

Written

Teaching tools

PC; video projector.

Office hours

See the website of Anna Gloria Billè