2019-2020

Data Visualization & Visual analytics

Credits: 
2
Hours: 
20
Area: 
Big Data Story Telling
Teachers: 
Rinzivillo Salvatore
Tutor: 
Fadda Daniele
Academic Year: 
2019-2020
2018-2019
2017-2018
2016-2017
Description: 

The module aims to present the basic methods and techniques for the visualization and presentation of the information obtained from different sources: structured data (relational, hierarchical, trees), network data (social network), temporal data, spatial data and spatio-temporal data. Studying the  existing methods and tools, some scenarios of visual analytics will be presented.

Pellungrini Roberto

Born and raised in Viareggio, Tuscany, Roberto Pellungrini is a PhD student in Computer Science at the University of Pisa. His main research interests concern ethical aspects related to Data Science, in particular regarding Privacy issues. Before winning the PhD scholarship, he attained a Master Degree in Business Informatics with a thesis on Assessing Privacy Risk and Quality in Human Mobility Data.

English

Moreo Fernández Alejandro

Alejandro Moreo Fernández received a PhD in Computer Sciences and Information Technologies from the University of Granada in 2013. He is a Postdoctoral Fellow at Istituto di Scienza e Tecnologie dell'Informazione ``A. Faedo'', which is part of the National Research Council (CNR). His research interests include deep learning and representation learning, with particular focus on imbalanced text classification, multilingual text classification, and cross-domain and cross-lingual sentiment classification.

English

Social Network Analysis

Credits: 
2
Hours: 
20
Area: 
Big Data Mining
Teachers: 
Passarella Andrea
Tutor: 
Rossetti Giulio
Academic Year: 
2022-2023
2021-2022
2020-2021
2019-2020
2018-2019
2017-2018
2016-2017
Description: 

This course introduces students to the theories, concepts and measures of Social Network Analysis (SNA), that is aimed at characterizing the structure of large-scale Online Social Networks (OSNs). The course presents both classroom teaching to introduce theoretical concepts, and hands-on computer work to apply the theory on real large-scale datasets obtained from OSNs like Facebook and Twitter.

Text Analytics and Opinion Mining

Credits: 
2
Hours: 
20
Area: 
Big Data Mining
Teachers: 
Esuli Andrea
Tutor: 
Moreo Fernández Alejandro
Academic Year: 
2019-2020
2018-2019
2017-2018
Description: 

This module introduces the main methods of analysis and mining of opinions and personal evaluations for users based on Big Data generated on the web or other sources. Emphasis will be put on text mining method applied to text originated on social media. Lessons will be supported by case studies developed in the SoBigData.eu lab.

Web Mining

Credits: 
2
Hours: 
20
Area: 
Big Data Mining
Teachers: 
Nardini Franco Maria
Perego Raffaele
Tutor: 
Trani Salvatore
Academic Year: 
2019-2020
2018-2019
2017-2018
Description: 

The course presents the main web data analysis techniques. By using the query log of a real search engine as a case study, students are guided in the development of a set of methodologies for data analysis aimed at creating the knowledge base for building a recommendation system. Then, the course discusses how the same information can be used to optimize the ranking in Web services. To this regard, the course introduces the learning to rank techniques aimed at estimating the relevance of objects with respect to specific user information needs.

Comandè Giovanni

Giovanni Comandè (LLM Harvard Law School USA, Ph.D. Scuola Superiore Sant’Anna) is Full Professor of Private comparative law at Scuola Superiore Sant’Anna where he also studied as ordinary student.

English

Milli Letizia

Letizia Milli holds Master Degree in Computer Science from University of Pisa, Italy graduated magna cum laude (110/110 cum laude) in 2013. 
She is currently a PhD student in Computer Science at the University of Pisa and a member of the Knowledge Discovery and Data Mining Laboratory (KDD-Lab), a joint research group with the Information Science and Technology Institute of the National Research in Pisa.
Her research interests include data mining, quantification, diffusion of phenomena and innovation in complex networks and the Science of Success.

English

Cresci Stefano

Stefano received his Bachelor’s and Master’s degrees in Computer Engineering from the University of Pisa respectively in 2007 and 2013. He also received a post-graduate Master’s degree in Big Data Analytics & Social Mining from the University of Pisa in 2016. Currently, he is a PhD Student at the Information Engineering School of the University of Pisa. He is also a Research Fellow at the Institute of Informatics and Telematics (IIT) of the National Research Council (CNR) in Pisa, Italy.

English

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