2016-2017

Data Driven Innovation

Credits: 
2
Hours: 
20
Area: 
Big Data for Business
Description: 

The module aims to show the main characteristics of the innovation processes in companies and institutions. After some basics of innovation economics, the management of the innovation processes will be presented (role of R&D, Open Innovation, etc.). The module also shows new innovation opportunities available after the last progresses in large scale data acquisition and elaboration, the basics of business models and start-ups. An exercise of business model innovation will try to explore che big data potential in opening new business opportunities.

Data Journalism & Story Telling

Credits: 
2
Hours: 
20
Area: 
Big Data Story Telling
Description: 

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.

Data Management for Business Intelligence

Credits: 
2
Hours: 
20
Area: 
Big Data Technology
Academic Year: 
Description: 

The module shows technologies and systems for accessing, managing and analysing Big Data for decision support. Technologies and analysis of problems are shown using examples and case studies in lab. The student will acquire skills on the main technologies for business intelligence and big data management, including data warehouse and online analytical processing technologies.

Data Visualization & Visual analytics

Credits: 
2
Hours: 
20
Area: 
Big Data Story Telling
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.

Trani Roberto

He graduated cum laude in 2016 in Computer Science at the University of Pisa (Italy), then he started in the same year the PhD in Computer Science at the same university. Roberto is currently a PhD student and a member of the High Performance Computing laboratory (HPC Lab) at the ISTI "A. Faedo" institute of CNR (Pisa). His main research interests are Information Retrieval, Machine Learning and Web Mining.

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

Internship

Credits: 
20
Hours: 
500
Description: 

The internship period is 500 hours, 20 CFU, that single student or a small group of students have to spend in one of the companies and institutions partner of the master, working on an agreed project and under the supervision of a team of tutors composed by the teachers of the master and the management companies.

Information Retrieval

Credits: 
5
Hours: 
40
Area: 
Big Data Sensing & Procurement
Teachers: 
Academic Year: 
Description: 

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.

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