Data Science Ph.D.

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Data Science is emerging as a disruptive consequence of the digital revolution. Based on the combination of big data availability, sophisticated data analysis techniques, and scalable computing infrastructures, Data Science is rapidly changing the way we do business, socialize, conduct research, and govern society. It is also changing the way scientific research is performed. Model-driven approaches are supplemented with data-driven approaches. A new paradigm emerged, where theories and models and the bottom up discovery of knowledge from data mutually support each other. Experiments and analyses over massive datasets are functional not only to the validation of existing theories and models, but also to the data-driven discovery of patterns emerging from data, which can help scientists design better theories and models, yielding deeper understanding of the complexity of social, economic, biological, technological, cultural and natural phenomena.

Data science is an interdisciplinary and pervasive paradigm aiming to turn data into knowledge, born at the intersection of a diversity of scientific and technological fields: databases and data mining, machine learning and artificial intelligence, complex systems and network science, statistics and statistical physics, information retrieval and text mining, natural language understanding, applied mathematics. Spectacular advances are occurring in data-driven pattern discovery, in automated learning of predictive models and in the analysis of complex networks.

Within this context, the Ph.D. in Data Science is aimed at educating the new generation of researchers that combine their disciplinary competences with those of a “data scientist”, able to exploit data and models for advancing knowledge in their own disciplines, or across diverse disciplines. To this purpose, the Ph.D. in Data Science develops a mix of knowledge and skills on the methods and technologies for the management of large, heterogeneous and complex data, for data sensing (how to harvest data), for data analysis and mining (how to make sense of data), for data visualization and storytelling (how to narrate data), for understanding the ethical issues and the social impact of Data Science. The Ph.D. students will have the opportunity of developing data science projects in a variety of domains, including:

  • Data science for society and policy
  • Data science for economics and finance
  • Data science for culture and the humanities
  • Data science for industry and manufacturing
  • Data science for biology and health
  • Data science for the hard and environmental sciences
  • Data science ethics and legal aspects
  • Data science techniques and methods

Applications from graduate students from any discipline are welcome. The successful candidate is expected to possess a solid motivation and personal preparation, and a strong propensity towards quantitative studies in own field.

Prenderà avvio con l’anno accademico 2021/2022 il primo Dottorato Nazionale in Intelligenza Artificiale coordinato da CNR e Università di Pisa e istituito con una convenzione firmata, oltre che dall’Ateneo pisano, anche da Sapienza Università di R

The ERC Advanced Grant XAI “Science & technology for the eXplanation of AI decision making”, led by Fosca Giannotti of the Italian CNR, in collaboration with the PhD program in “Data Science” by Scuola Normale Superiore in Pisa, invites applic

ISTI News June 2017

On the second issue of the ISTI News newsletter you'll find a description of the new Ph.D.

Data Science - 16 giugno 2016

The increasing interest on data science from both the academic and industrial worlds led to the creation of a brand new Ph.D.

PhD Course in Data Science

27 June 2017

Italia Oggi Sette
Interpretare i Dati

26 June 2017

La Stampa TuttoScienze
A Pisa il primo dottorato per «data scientist»

21 June 2017

Il Tirreno
Il futuro è degli scienziati dei dati

17 June 2017

A Pisa nasce il Ph. D in data science

16 June 2017

Lucca in Diretta
Scienza dei dati, nasce il nuovo dottorato a Imt

16 June 2017
Web Site
Start Date
1 November 2017
Internal Project
Department of Computer Science, University of Pisa (DI-UNIPI)
Istituto di Scienza e Tecnologie dell’Informazione, National Research Council of Italy (ISTI-CNR)