2019-2020

Statistical Methods for Data Science

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.

Data Driven Innovation

Credits: 
1
Hours: 
12
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.

Information Retrieval

Credits: 
4
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.

Santoro Roberto

As graduate researcher at the university of Pisa, Roberto worked initially on algorithms for similarity measures and classification on knowledge graphs (Google Research Award), and later on algorithms for economic networks. He then moved into industry: in Spaziodati he works both on the development of semantic annotation engines and on data science/machine learning projects applied to large economic and textual datasets. In SpazioDati he is also in charge of the SmartDataLake EU project.

English

Del Sarto Nicola

Nicola Del Sarto is a post-doctoral research fellow at Scuola Superiore Sant'Anna in Pisa. He received a Ph.D in Management from Scuola Superiore Sant'Anna in 2019. Nicola's research interests focus on start-ups and support mechanisms such as incubators, accelerators and corporate accelerator programs. Moreover, he is investigating the processes of business creation under the Open Innovation paradigm. Nicola holds a Master degree in economics from University of Pisa and a post graduate master in Management, innovation and engineering of services from Scuola Superiore Sant'Anna.

English

Cucino Valentina

Valentina Cucino is a Postdoctoral Scholar at Scuola Superiore Sant’Anna, Pisa. She received her PhD in Management Innovation, Sustainability and Healthcare from Scuola Superiore Sant’Anna in 2019. Her research interest deals with technology transfer, new business venturing and human resource management.

English

Borselli Alice

Borselli

I attended the University of Pisa and got the master's degree in Mathematics in 2009. I was a PhD fellow at the Scuola Superiore Sant’Anna in Pisa on "Innovative Technologies of ICT and Robotics" for one and a half years. During the PhD I attended some programming courses and I finally found a job as software developer in Pisa. Recently I started to get involved in data analysis and machine learning, so I decided to enroll in the Big Data Analytics and Social Mining Master.

2019-2020

Calcagno Valentino

Calcagno

In March 2017, I graduated from "La Sapienza" University of Rome with a master's degree in sociology discussing a thesis on the topic of representation of work among young people in Rome. In the same year, through an internship at the CNR, I partecipated in a research about the construction of the image of migrants in online newspapers and textbooks. The interest in the understanding of social dynamics led me to decide to expand my knowledge with the master in Big Data Analytics and Social Mining. I believe that the application of techniques and models deriving from data science can highlight further and new aspects in the study of social phenomena to grasp their contradictions and their problems.

2019-2020

Vasciaveo Giulia

Vasciaveo

I graduated in Computer Engineering at the Politecnico di Torino, with a thesis on Machine Learning applied to Big Data. I immediately found a job at a company in the data analysis field, offering technological support to the data scientist team and involved in projects for the implementation, development and automation of procedures for Data Integration, Transformation and Visualization as well as the organization of the data infrastructure. Afterwards I thought it was appropriate to learn in deep the topics of Big Data by enrolling in this postgraduate master, in order to develop new skills in the field that fascinates me the most and in which I would like to find work opportunities.

2019-2020

Gabrieli Noemi

Gabrieli

After completing a Master's Degree in Biomedical Engineering at the University of Pisa, I worked as an R&D Engineer in a biomedical company-university spin-off contributing to the European Horizon2020 Endoo-EU project. In this innovative and stimulating context, my curiosity towards Data Science's world was born. Later, as an SQA Engineer for a multinational company, I continued to inform myself about Data Science and its infinite application possibilities with the aim of transforming my personal interest into a real job opportunity. Hence the decision to enroll in the Master in Big Data Analytics & Social Mining.

2019-2020

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