Management Science

Data Science with Complex Networks

Complex Systems are everywhere and in the era of massive production of electronic data coming from all sort of devices it is of crucial importance to have the right tools to manage and extract from them all the valuable information. To this aim during this course we will develop both the basic theoretical tools and the practical coding technics to tackle all sort of complex systems, ranging from Trade and Financial Networks, to the World Wide Web and the Social Networks.

Computer Programming and Methodology

This course aims at introducing to students principles and methodologies of computer programming. Emphasis is on good programming style, techniques and tools that allow efficient design, development and maintenance of software systems. The course focuses on the design of computer applications drawing attention to modern software engineering principles and programming techniques, like object-oriented design, decomposition, encapsulation, abstraction, and testing. A significative case study is used to allow students to experiment with the principles and techniques considered in this course.

Basic Numerical Linear Algebra

The course is aimed to introduce the basic notions about vector spaces, vectors, matrices, and norms, along with the basic numerical methods concerning the solution linear systems. In particular: direct methods for square linear systems and conditioning analysis; direct methods for solving over-determined linear systems in the least square sense, with applications. The course also provides an introduction to Matlab, which is used for implementing the methods.

Advanced Topics of Econometrics

The main goal of this course is to provide an introduction to/review of the fundamental theoretical concepts and applications of modern econometric techniques used in empirical social sciences. TOPICS covered in the course include: 1) Conventional Methods to Estimate Causal Effects: OLS and IV 2) The Angrist-Imbens-Rubin approach (LATE-IV) 3) Further IV issues and Regression Discontinuity Design (RDD) 4) Matching methods for the estimation of causal effects 5) Differences-in-Differences estimation