B0071 - STATISTICA PER L’ANALISI DEI BIG DATA

Learning outcomes

The course aims at providing the student the necessary tools to configure, analyze and interpret big data. At the end of the course the student will be able to understand the fundamental principles on which the systems for the management of Big Data are built; identify the peculiar features and functionalities offered by each of these systems; critically evaluate the sources from which the data is extracted; use the appropriate statistical tools for their analysis.

Course contents

• Introduction to supervised statistical learning
• Multiple linear regression
• Dimension reduction and regularisation
• Classification
  
• Tree-based methods
• Artificial neural networks

Readings/Bibliography

Suggested readings:

• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). Introduzione all’apprendimento statistico con applicazioni in R. Piccin. ISBN: 978-88-299-3094-4

English version freely available online:

• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to Statistical Learning. Second Edition. New York: Springer. ISBN: 978-1-0716-1417-4. E-book ISBN 978-1-0716-1418-1

Teaching methods

Lectures and practical sessions.

Assessment methods

Written exam aimed at assessing the student's acquired knowledge and ability to analyze and interpret big data using appropriate statistical tools. For all students (regardless of the fact that they have attended lectures or not) the test consists of 5-7 questions, both multiple choice and open. The final grade is out of thirty.

Teaching tools

• Slides
• Scripts
• Datasets

Office hours

See the website of Marco Berrettini