Python

The module aims at preparing students to the approprieted presentation of data and knowledge extracted from them through visualization tools and narratives that exploit multimedia.
The module first presents the basic visualization techniques for the effective presentation of information from several different sources: structured data (relational, hierarchies, trees), relational data (social networks), temporal data, spatial data and spatio-temporal data.

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 aims to teach the software modules used to build a modern search engine and the analysis of the performance and the computational limits of the algorithmics solutions currently used in each of them. Practical and theoretical fundmentals for the organization and the analysis of IR systems.

The objective of the module is to align the competences of the students in computer science and base, in particular about databases, structured data analysis, statistics and programming languages.

The module aims to teach the software modules used to build a modern search engine and the analysis of the performance and the computational limits of the algorithmics solutions currently used in each of them. Practical and theoretical fundmentals for the organization and the analysis of IR systems.

The aim of this course is to introduce the student with the high performance Big Data management tools. The student will gain expertise in the use od NO-SQL platforms for the analysis and mining of large data volumes, thus performing tasks that would not be feasible with traditional data bases.

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 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 objective of the module is to align the competences of the students in computer science and base, in particular about databases, structured data analysis and programming languages.

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.