University of Commerce Luigi Bocconi

This course introduces the analysis of political processes in well-established democracies. It focuses particularly on the topics of mass political behavior in contemporary democracies (such as voting participation, protest, and vote choice) and the impact of electoral, legislative, and executive institutions on policy outcomes. One of the central goals in the course is to evaluate whether the quality of political representation in contemporary democracies is as high as it could/should be and to come up with ways to improve it. Students are expected to become familiar with important theoretical arguments in the study of mass behavior, political institutions, and representation in advanced democracies; to be able to assess whether these arguments are supported by systematic empirical evidence; and to apply knowledge learned in the course to understand current political events and trends in democratic nations across the world. 

This course develops an understanding of the reasons, the range, and the extent of business-government relations, and develops concrete skills in order to effectively manage these relations. Through an interactive approach, the course shows the empirical application to healthcare sector. More specifically, the course illustrates the relations between the industry of medical technologies and public administration as to the several strategies of market access as a concrete and interesting case of how managing business government relations becomes crucial to succeed from both sides. The case of the healthcare sector covers a relevant part of the whole course because it is highly representative of public-private interrelations and lends itself to be representative of different jurisdictions.

The course is a rigorous introduction to probability. Students gain a solid grounding on the its foundations, learn how to deal with randomness with the correct mathematical tools and how to solve problems. Course topics include probability; definition and properties; conditional probability and independence; random variables and random vectors; joint and conditional distributions; expectation and moments; integral tranforms; convergence in distribution and the Central Limit Theorum; and modes of convergence and the laws of large numbers. Prerequisites: Set theory, sequences and series, continuous and differentiable functions, and integrals.

This course analyzes the economic issues related to the process of European integration through a theoretical and a policy perspective. Through the study of economic integration theories and the main European Union (EU) policies, students are able to acquire a critical perspective of the basic issues leading to the development of the European Institutions and the different historical phases of the European Union, ultimately giving birth to the Economic and Monetary Union. The course is composed of three parts. The first part of the course is devoted to the study of the theory of economic integration, considering the market of goods (customs unions, free trade areas), of factors of production (common markets) and the coordination of economic policies (economic union). A link is made between the theoretical instruments and the actual evolution of the European integration process. The second part explores the main European policies (common objectives, instruments, and financial means), their evolution over time and the corresponding mechanisms of governance. The third part of the course is centered on the Economic and Monetary Union and its effects for the European economy. 

The course uses experiential learning to develop the skills most critical to success in today's business landscape: designing and planning innovative business models. The course offers an industry focus on fashion, luxury, and retail, and integrates key topics such as sustainability and digitalization. This course explores how to design innovative business models with the support of a proprietary simulation software that allows to develop a practical approach integrating creative ideas with competitive and financial dimensions. Students can see the immediate consequences of their decisions and learn what it’s truly like to juggle competing priorities amidst a constant influx of information provided by the professors. The learning process is enhanced by the collaboration of external guest speakers and a start-up accelerator.

This course analyzes advanced management practices in complex business environments and offers a comprehensive introduction to Lean Management, a model derived from the diffusion of the principles and tools originally developed at the Toyota Motor Company. The course emphasizes the organizational, managerial, and human aspects of Lean Thinking and illustrates its adoption in a variety of industries and business functions. It analyzes how firms should design and implement lean systems and offers a framework to undertake and sustain lean transformations. It illustrates how the adoption of Lean Thinking principles and tools affect managerial decision making and problem solving, eliminating waste and variability. It also elaborates on recent trends such as the application of lean thinking to innovation (Lean Product and Process Development), to entrepreneurship (Lean Startup Method), to sustainability (Lean & Green), as well as its connection to the Agile movement.

This course provides an advanced understanding of the python programming language and its main features through various applications in many fields. Students use procedural and object-oriented programming language concepts in real programs; combine programming techniques to solve problems of varying degrees of difficulty in applied fields; find and understand programming language documentation to learn new information needed to solve programming problems; and implement problem solving strategies. Course topics include input/output in Python, classes, databases management with Python, computer simulations, and agent-based modeling. Prerequisites: an introductory course on python programming or similar language (e.g. Java, C, etc.).

Public institutions and non-profit organizations play a fundamental role in today's economic systems as policy designers, public service-providers, grant-makers, and advocates of unmet ever-changing human needs. As their goals are different from those pursued by business companies, managers working in these organizations perform different tasks, follow different logic and must develop different skills. At the end of this course students understand these differences, are able to assess how successful public/nonprofit managers behave, are aware about which mistakes to avoid, and apply this learning to perform basic tasks connected to the overall creation of public value that public/non-profit organizations produce for their communities. The course is divided into 2 parts. Part 1: Public policy and public management (governments and their structure; role of bureaucracies; types of bureaucrats and how to motivate them; decision-making and performance evaluation in public agencies). Part 2: Non-profit management (role of the third sector in the economy; types of non-profit organizations: an overview; governance and management of non-profit organizations; how to secure contributed income: key successful factors in fundraising).

This course provides: (i) a working knowledge of the most important aspects of the linear regression model; and (ii) basic tools needed to understand and critically interpret empirical research conducted by others as well as to plan and conduct empirical analyses using economic data. The key concepts of the underlying statistical theory are covered, but major emphasis is placed on application of the theory from a practical standpoint. The course also provides an introduction on how to conduct empirical analysis of economic data using Stata, a statistical software package. Prerequisites: Sound knowledge of mathematics and statistics.

This course aims at providing the basic theoretical and applied tools for a rigorous statistical analysis. Specifically, the course focuses on techniques to summarize and visualize data of different types and their possible relations, as well as on basic sampling and inferential procedures, and on the assessment of the risk associated to extrapolation and inference. In particular, students learn how to extract information from data and how to assess the reliability of such information. The course covers the following topics: collection, management, and summary of data using frequency distributions, graphical representations, and summaries; study of the relationship between two variables; statistical inference and sampling variability; theory of point estimation and confidence intervals; hypothesis testing; and simple and multiple regression models. All the descriptive and inferential tools introduced during the course are applied to data using the statistical software R - and in particular the integrated development environment (IDE) RStudio. Prerequisites: understanding of the concepts of probability theory and random variables.