Big Data Mining

Mobility Data Analysis

Credits: 
2
Hours: 
20
Area: 
Big Data Mining
Description: 

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.

Social Network Analysis

Credits: 
2
Hours: 
20
Area: 
Big Data Mining
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.

Social Network Analysis

Credits: 
3
Hours: 
21
Area: 
Big Data Mining
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
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.

Sentiment Analysis & Opinion Mining

Credits: 
2
Hours: 
20
Area: 
Big Data Mining
Teachers: 
Academic Year: 
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.

Sentiment Analysis & Opinion Mining

Credits: 
3
Hours: 
21
Area: 
Big Data Mining
Teachers: 
Academic Year: 
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.

Mobility Data Analysis

Credits: 
3
Hours: 
21
Area: 
Big Data Mining
Teachers: 
Academic Year: 
Description: 

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.

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