Course Units

The Theories pillar includes theoretical courses in Micro-Macroeconomics and Theories of Management.

• Management Theories: Implications of Business Economics, Finance, Organization, and Marketing (42 hours; first-year; in Italian)
  The course aims at conveying the theoretical foundations of management disciplines such as business economics, finance, organizational theory, and marketing. The objective is to support doctoral students in identifying the reference framework for designing their individual research projects.
• Micro-Macroeconomics  (42 hours; first-year; in Italian)
  The course gives doctoral students an introduction to advanced topics in micro and macroeconomics and is divided into two modules. The first module entitled “Production, Consumption and General Equilibrium” and the second module entitled “Growth Economy and Monetary Policy” .
• Innovation in the global economy (24 hours; first-year; in English) 
  The course focuses on the management of innovation in the global economy and aims to provide doctoral students with an in-depth analysis of the different areas of literature dealing with this economic phenomenon. 

The Methods pillar includes courses related to bibliographic research, research design, quantitative and qualitative research techniques, and qualitative and quantitative data analysis tools.

• Databases for bibliographic research  (9 hours; first-year; in Italian)
  The course covers the processes and methods of publishing research tools for analysis of academic literature through databases.
• Introduction to Phyton and Latex (24 hours; first-year; in Italian)
  The course aims at providing doctoral students with the fundamentals of programming in Python and at studying in deep some standard tools for data analysis. The specific applications vary based on the research needs and interests of the participants.
• Mathematical methods for data analysis (22 hours; first-year; in Italian
  The course aims to provide useful mathematical tools for tackling advanced data analysis techniques, through the study of linear algebra.
• Statistical methods for the analysis of economic and business data (36 hours; first-year; in Italian)
  The course consolidates knowledge of advanced statistical methodologies relevant to economics and business sciences and typically applicable to big data. The course consists of two modules.
• Qualitative research methodology (21 hours; first-year; in English)
  The module presents research design and offers in depth focus on qualitative research methodologies.
• Quantitative research methodology (21 hours; first-year; in Italian)
  The course extends student knowledge on the collection and analysis of primary and secondary data through different quantitative research techniques.
• Data management with STATA (21 ore; primo anno; in English)
  The objective of the course is to introduce doctoral students to STATA, providing them with the basics related to the fundamental syntactic structure and the main commands for data management, analysis, and graphical representation.
   

The Applications pillar is declined according to students' teaching needs through lectures and seminars consistent with the chosen research topic.

• Seminars for the research deepening in Micro-Macroeconomics (first and second year)
• Seminars for the research deepening in Management (first and second year)
• Seminars for the research deepening in Economics History (first and second year)
• Seminars for the deepening of data analysis techniques for social sciences (first and second year)
• In-depth seminars in: Combined Permutation Tests for Economics and Business with applications in R
   

Other activities including courses aimed at developing soft skills, language skills and computer skills complete the training.

• Language Enhancement
• Fundamentals of ethics (ITA)
• Computer enhancement
• Research and knowledge management of European and international research systems
• Enhancement and dissemination of results, intellectual property and open access to research data and products
• Laboratory activities