In this course we start from the basic notions of graph theory and self-similar phenomena in order to correctly analyse large socio-economic networks. From this analysis we then proceed in the description of the modelling for various classes of phenomena and to the correct definition of benchmarks through an approach inspired by classical statistical physics.
This module presentes techniques and methods for acquisition of Big Data from a large sources of data available, including mobile phone data, GPS data, customer purchase data, social network data, open and administrative data, environmental and personal sensor data. We discuss also several participatory methods for crowdsourcing or crowdsensing collection of data through ad hoc campains like serious games and viral diffusion.
The module si composed by several Seminars on experiences and case- and use-studies of Big Data analytics and Social Mining from the SoBigData.eu labs and from the companies and institutions that are partners in the Master.
The module aims to introduce ethical and legal notions of privacy, anonymity, transparency and discrimination, also referring the Directives and Regulations of the European Union and their ongoing evolution. Tho module will show Privacy-by-Design models and technologies that are useful to protect the users' rights and that allow the analysis of Big Data without harming the right to the protection of personal data, to transparency and to a fair treatment.
Leonardo Nizzoli was born in Pisa in 1984. After graduating in Physics in 2010, he worked more than 5 years as a researcher in a R&D company devoted to the implementation of marine wave energy converters. In 2016 he attended the Post Graduate Master in Big Data Analytics and Social Mining at the University of Pisa. He spent the training period at Enel Global Thermal Generation, where he realized - in collaboration with Angela Italiano - a data driven machine learning model able to predict thermo-acoustic instabilities in natural gas turbines.
The purpose of the course is to introduce the main analysis techniques for spatio-temporal data, with a particular focus on human mobility (including vehicles), aimed to better understand the overall mobility of a territory. The presentation will be supported by several case studies developed with the SoBigData.eu laboratory.
The module aims to teach how to present the knowledge extracted from big data using multimedia story telling. It also shows some of the most recent and meaningful experiences of journalism and story telling based on quantitative information extracted from different data sources.
Born in Salerno (Italy), I earned my PhD in Computer Science at University of Pisa with the thesis "Human Mobility, Social Networks and Economic Development: a Data Science perspective". In my research, I exploit the power of Big Data to study many aspects of human behavior: the patterns of human mobility, the structure and evolution of complex networks, the patterns of success in sports, and the usage of data-driven measures of human behavior to monitor and predict the economic development of countries, cities, and territories.
Angelica Lo Duca is a postdoctoral researcher at the Institute of Informatics and Telematics of the National Research Council of Pisa. In 2012, she received her Ph.D. in Ingegneria dell'Informazione from the University of Pisa. She received her Bachelor's and Master's degrees in Computer Engineering from University of Pisa respectively in 2005 and 2007. Currently, she works at the Web Applications for the Future Internet Laboratory, in the Semantic Web and Data Visualization group.
The module presents technologies and systems for designing, populating and querying Data Warehouse for decision support. The emphasis is on technologies and analysis of application problems by using examples and case studies. The student will acquire knowledge and skills on major technologies for Business Intelligence such as ETL (Extract, Transform and Load), Data Warehousing, Analytics SQL, OLAP (Online Analytical Processing).
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