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Course objectives

The primary objective of the PhD program in Statistical Sciences is to provide students with a tailored training program that integrates a solid methodological foundation with knowledge of specific topics relevant to their research.
Specifically, the program provides basic and advanced skills in data collection, management, and analysis, creating professionals suitable for employment in research centers, both at universities and other public and private institutions, both nationally and internationally.
More specifically, the program aims to train PhDs with solid skills and high levels of professionalism in the management, analysis, and statistical modeling of data, preferably large data (Big Data), which are now ubiquitous in modern research and work environments in the field of Data Science. For example, these skills are essential in "Industrial Analytics" as well as in "Cloud Manufacturing," where purely statistical knowledge is complemented by IT tools for managing and analyzing large amounts of data and using them to support decision-making. This multidisciplinary approach has become a standard feature of modern statisticians. The Faculty is committed to providing students with the necessary knowledge through first-year courses (for example, the courses Statistical Models and Programming Methodologies for Data Analysis) and to promoting research topics that involve the development of methods and models for high-dimensional data analysis and/or empirical analysis of large data sets, in a wide variety of application fields, from economics and finance to social and environmental statistics and demography.

Educational activities

The study project of the doctoral course is carried out according to the following steps.

Start of activities. On arrival, students are informed of the regulations and the course programme for the first few months. Each student is assigned a tutor, who is available to help and guide them in their choice of research topic for the second and third year.

First-year programme. The basic first-year programme comprises a core of five compulsory courses covering advanced mathematics, probability theory, computer programming, statistical theory and modelling. In addition, a number of specialised modules are offered, covering a range of more advanced topics. A side objective of the courses during the first year is to train students in group work, seminar presentations and the preparation of scientific papers. At the end of the first year, the Academic Board assesses doctoral students for admission to the second year. Admission is conditional on achieving a satisfactory level in the first year's activities. For each of the five core modules, assessment is based on a final examination. By September of the first year, students admitted to the second year propose to the Academic Board their research programme to be developed during the second and third years. Students may join local research groups or start an independent research project. The PhD programme and the Department of Statistical Sciences can provide a suitable research environment for this. Broad areas of research include: statistical methodology and its applications; statistical and econometric methods; social statistics; demography. After approval of the project, the Academic Board assigns a supervisor to each PhD student.

Research programme in the second and third year. The research activity in the second and third year is the distinctive feature of the PhD programme and is aimed at achieving independent research capabilities. During the second and third years up to 12 months may be spent at a university or other highly qualified institution abroad. Students are strongly encouraged to include a period of research abroad in their programme, taking advantage of the national and international collaborative networks of the members of the Doctoral Board.

The result of the research must be presented as a thesis containing original scientific results relevant to the field of statistics and its applications.

Research areas

Broad areas of research include:

- Statistical methodology and its applications. Methodological aspects range from statistical models to inference and computational issues. Applications may concern a variety of fields such as technology, industry, finance, biology, medicine, environmental studies, etc.

- Statistical methods and applications in Economics. In particular: time series analysis, forecasting, statistical methods for labour economics and evaluation of public policies.

- Social Statistics. In particular, survey methodology, models for individual ad aggregated data, segmentation techniques, multilevel models.

- Demography. In particular, population structure and dynamics, statistical analysis of demographic behaviours and policies.

Professional profile

The Doctoral Program in Statistical Sciences provides basic and advanced specialized skills in data collection, management, and analysis. This training creates professionals suitable for placement in university research centers and other public and private institutions, both nationally and internationally. This is demonstrated by the placement of several PhD graduates at prestigious organizations (Harvard University (USA), Oxford University (UK), Berkeley University (USA), the United Nations (UN), and the Organization for Economic Cooperation and Development (OECD). See Table 1 at http://www.stat.unipd.it/ricerca/dottorato-di-ricerca for statistics on the career opportunities for PhD graduates in statistics, as well as the list of PhD program alumni and their most recent positions at https://www.stat.unipd.it/ricerca/dottori-di-ricerca.

  Other information

• Calls and Admissions
• Administrative information
• The International PhD guide