English

This course is unique in its focus on the core challenges facing our increasingly 'smart' cities, from their operational functions and planning through to management and control. The course reflects the changes that technology is making to the operation of, and our understanding of, the city, and gives students the technical and theoretical skills needed to make a difference to the cities of today and tomorrow.

The current uncertain times are marked by political upheavals, rapid technological change, and ecological loss and crisis. Yet, this perception of uncertainty is not unique to the present. How have people made sense of the unknown in the past? How have they tried to predict, control, or survive uncertain futures? This seminar explores how individuals, communities, and institutions have historically responded to uncertainty, in North America and beyond. Seminar topics therefore include religious beliefs and prophecies, narratives of destiny and utopia, science and statistics, social planning, bureaucracy and record-keeping, violence and exclusion, art, sports, as well as turns to history itself. Furthermore, the class discusses how historians themselves deal with uncertainty in their work: from gaps in the archives and collective memory, to epistemological questions, biases in historical research, and contested interpretations of the past. Through these themes, students are introduced to the foundational skills of studying history: how to ask critical questions, develop an argument, read primary and secondary sources, and how to write (about) history. A field trip to a local archive offers practical insights into what it means to work as a historian, and the uncertainties that come with it.

This course explores the fundamental nature of the main groups of materials which constitute planets such as the Earth, and develops an understanding of how atomic structure of materials ultimately defines planetary processes. In Part A: From atoms to minerals, students briefly review atomic theory, consider how atoms are arranged in crystalline materials and how this, ultimately, controls material properties. Interaction of crystalline materials with light, X-rays, and electrons are used to introduce the theoretical and practical basis behind analytical techniques used to study Earth and planetary materials. In Part B: Planetary building blocks, students review the main groups of solid materials which constitute planets such as the Earth, considering how structure, chemistry, physical properties, and occurrence are interrelated. In Part C: Modelling chemical processes, students consider how the stability and occurrence of materials can be predicted and determined numerically, and consider factors governing the rates at which natural processes occur.

In order to create a learning environment that promotes the wellbeing of all within it, there is a need for capacity building so that everyone has the skills, knowledge, and disposition to be able to make a positive contribution to the health promoting environment. This course seeks to serve that function, having the potential to enhance student wellbeing and student ability to thrive in university and achieve success in their studies, through increasing students' capacity to be well and so contribute positively to a healthy environment and ethos in which to study. 

This course explores the architecture and politics behind global finance, and how it can be reformed to address the world's most urgent needs: climate change, inequality, debt sustainability, and beyond. The challenge today isn't just what we fund, but how we fund it. Through a mix of academic literature, case studies, student-led presentations, and guest lectures by senior experts from leading institutions, the course explores which actors shape the global economy, how they wield their influence, and what it would take to reform them.

The course develops students' understanding of the classical linear econometric model (ordinary least squares). It covers a range of topics, including estimation and inference in multivariate regression models; the use of limited dependent variables; large sample properties of OLS estimators; multicollinearity and heteroskedasticity. The course develops students' applied skills through the use of appropriate econometric software.

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrollment is by permission of the instructor. This course focuses on the main data mining methods used in knowledge discovery in business employing internal and external data. With an emphasis on data analysis and on the use of a software, special attention is devoted to techniques that help to single out the relationships of interdependence and patterns in business and market research phenomena. Students learn, hands-on, how to organize and analyze market research data. In particular, at the end of the course students are able to: independently run a complete data mining process (from data pre-processing to the interpretation of obtained results); choose the best suited statistical methodology for the problem at hand; to critically interpret empirical results.

The course content is divided as follows:

1. INTRODUCTION: data-analytic thinking, overview of Data Mining, from business problems to Data Mining tasks, the Data Mining process; real-world business challenges.

2. DATA EXPLORATION AND PREPARATION: data objects and attributes type, data matrices and their transformations, data cleaning.

3. STATISTICAL AND DATA MINING SOFTWARE: introduction to SAS; SAS LAB tutorial on data organization  and data preprocessing using real datasets.

4. MULTIDIMENSIONAL DATA ANALYSIS & DIMENSIONALITY REDUCTION: Principal component analysis and its variants (e.g., PCA of ranks); Multiple Correspondence Analysis - categorical pattern detection. Theory and practice with SAS.

5. PROXIMITY MEASURES: distance and similarity for mixed data.

6. CLUSTERING: hierarchical, partitional and hybrid clustering. Understanding the Results of Clustering.

7. PROFILING: deriving typical behavioral segments. 

8. CO-OCCURRENCES AND ASSOCIATIONS: Finding items that go together. Theory and application of main association rules algorithms in SAS.

9. Data Mining SCORING: Theory and practice.

10. Causal ML and Advanced Lab:  causal inference fundamentals; application of causal ML algorithms in the context of business analytics for decision support; evaluate a marketing campaign using causal ML in SAS; targeting and interpreting causal results.

This course provides the skills needed to critically evaluate brain-related information from diverse sources and engage in evidence-based discussions. This knowledge and ability to analyze complex neuroscientific concepts can be highly valuable for you as a future leader, enabling you to make informed decisions, understand human behavior, and effectively communicate with others in areas related to neuroscience and its implications.

This course examines the core theories and concepts of managing human behavior in organizations. It covers a variety of theories and concepts to provide a foundational understanding of the attributes of individual behavior in organizations, including personality, motivation, decision-making, as well as interpersonal behaviors, including teamwork, power and influence, leadership, and communication.