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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. 

This course focuses on race, discrimination, and racial inequalities. The course addresses three key questions: what is race as perceived in the U.S. and Europe, and what are the sources of racial inequalities; what does social science research tell us about patterns and trends of racial inequalities; and what policies can alleviate racial inequalities? The course systematically adopts comparative perspectives focusing on the North American and European contexts. It also addresses research on race and racial inequality within an interdisciplinary lens particularly building on sociology, economics, and social psychology.

This course examines media through a social justice lens – revealing hidden costs and social, political, cultural implications of emerging media technologies and longstanding media practices. It covers key concepts and theories from media studies, journalism studies, cultural studies, sociology, and criminology, with an emphasis on First Nations knowledges and critical approaches to race and gender. 

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrollment is by permission of the instructor. By the end of the course, students know the material sources available for reconstructing the production processes and the function of objects and structures found in the archaeology of the ancient world, coping with the technical and anthropological aspects. They recognize and critically examine the archaeological documentation relating to production sites and places of consumption in ancient finds; they also know how to identify the diagnostic elements of material culture and have the tools to frame information in its correct chronological, historical, political, and social context.

The course deals with Archaeology of Production in both theoretical and practical perspectives. A diachronic overview is offered, focusing the examples on the Mediterranean and European areas in Antiquity. The course is broadly divided in three blocks of lessons:

1. Theoretical and methodological issues

2. Craft processes

3. Thematic studies related to the discipline

Readings and discussions, visits to museums and laboratories with hands-on activities, and seminars are scheduled during the course.

This course asks whether the welfare state is justified, how extensive it should be, and what level of inequality and/or poverty is acceptable in a just society. Thus, the course examines the main theories of distributive or social justice in contemporary analytical political philosophy. Distributive justice is about the fair distribution of burdens and benefits in a society and some of the main approaches to this issue that we look at include liberal egalitarianism, libertarianism (left and right), luck egalitarianism, and relational egalitarianism and republicanism. The course also looks at some of the critiques, alternative approaches and applications of these theories, to areas like health and education.

This course provides students with a solid grounding in various aspects of software engineering process related to building large software systems. The course covers various aspects related to building software systems ranging from the use of software lifecycle models, to project management, to large-scale software architectures. Specifically, software lifecycle models, including variations of the waterfall and spiral models as well as extreme programming and agile, are introduced along with concepts that are relevant to the specific model stages. These concepts include domain analysis, requirements and specification analysis, testing and debugging, and version control. Moreover, strategies for managing large software projects and their contracts as well as project teams are presented and contrasted.

The objective of this course is for students to learn to appropriately apply discrete event simulation modeling for decision support in Industrial Engineering problems through developing skills in model building, simulation output analysis, and communication of technical information and conclusions drawn from data analysis. Topics include Introduction to Discrete, Event System Simulation, Simulating a Queueing System, General Principles, Discrete Distributions & Continuous Distributions, Poisson Process and Characteristics of Queueing Systems, Long-Run Measures of Performance of Queueing Systems, Steady-State Behavior, Networks of Queues, Techniques for Generating Random Numbers, Tests for Random Numbers: Tests for Autocorrelation, Inverse-Transform & Acceptance-Rejection Techniques, Parameter Estimation, Data Collection & Identifying the Distribution with Data, Multivariate and Time-Series Input Models, and Stochastic Nature of Output Data.

This course examines the social and economic impacts of artificial intelligence (AI), with a focus on ethical considerations, social justice, and both the opportunities and risks associated with AI technologies. Through hands-on, active learning exercises, participants use AI tools to complete practical tasks while developing hybrid critical thinking skills that integrate human judgment with generative AI. The course emphasizes responsible and effective AI use and is assessed through a combination of group projects and individual reflection.