Big Data Mining

The course presents the main elements for understanding financial markets, their structure, and technological infrastructure. Specifically, the course provides a background on basic empirical modeling of financial time series, from low to ultrahigh frequency, identifying the key data science aspects including data storage, latency, high dimensional inference, etc. It also covers semantic analysis of texts from news feed and social networks for financial forecasting.

The module provides an introduction to base concepts of data mining and knowledge extraction process, introducing analytical models and algorithms for clustering, classification and pattern discovery, also referring Big Data sources.

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 provides an introduction to base concepts of data mining and knowledge extraction process, introducing analytical models and algorithms for clustering, classification and pattern discovery, also referring Big Data sources.

The module provides an introduction to base concepts of data mining and knowledge extraction process, introducing analytical models and algorithms for clustering, classification and pattern discovery, also referring Big Data sources.

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.

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.

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.

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.

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.