Statistics

This course offers a study of the key concepts and methods of Statistical Learning by focusing on regression and classification in high-dimensional settings. Students model and analyze complex data, apply supervised and unsupervised learning techniques, and use computational tools for data analysis. This course puts special emphasis on problem formulation, variable selection, and practical implementation using modern software. 

Pre-requisites: Basics of Statistics 

This course offers a study of the principles and techniques of statistical graphics and data visualization. It discusses how to select and create effective visual representations for univariate, bivariate, and multivariate data. Topics include: graphical perception; the grammar of statistical graphs; exploratory data analysis; advanced data exploration such as maps and network charts; practical applications in statistics. 

The course is designed for senior undergraduates that are interested in qualitative methods and of some research experience. The course introduces two major approaches to analysing qualitative data, namely, grounded-theory based coding approach and chronological sequence-based process data analysis. As well, the course also covers related topics to provide comprehensive guidance to students, including the philosophy of qualitative methods, collection of qualitative data, reporting qualitative findings, and ethical issues in qualitative data analysis.

This course builds on Stochastic Processes I and introduces an array of stochastic models with biomedical and other real world applications. Topics include Poisson process, compound Poisson process, marked Poisson process, point process, epidemic models, continuous time Markov chain, birth and death processes, martingale. The course requires students to take prerequisites.

This course teaches liberal arts students to understand the basic notions of probability theory and statistics, and to be able to comprehend the meaning of an elementary statistical analysis. While some mathematics is unavoidable to handle probabilities and statistics, the course focuses on comprehending simple analyses concerning randomness, subjective and objective probabilities, parameter estimation, confidence. After a short introduction of elementary probability theory, the most important discrete and continuous distributions, the law of large numbers and the central limit theorem, it discusses the basics of statistics, parameter estimation, confidence, and Bayesian statistics.

This course introduces basic statistical concepts to life science students. It provides a conceptual understanding of statistical methods with the help of user-friendly software instead of complicated derivations. Topics include basic numerical and graphical descriptive statistics, basic study designs, estimation and hypothesis testing for population proportions and population means, linear regression, as well as other selected topics. Real cases in life sciences are used to present the materials. 

This course introduces the mathematical, statistical, and computational challenges in natural language processing. It covers the main applications of NLP techniques and a range of models in structured prediction and deep learning. Students gain a thorough introduction to cutting-edge machine learning and deep learning techniques for NLP. This course covers a broad range of topics including text classification, sentiment analysis, neural network, word embedding, sequence models, language models, machine translation, topic detection, and ChatGPT. The underlying techniques from probability, statistics, machine learning, transformer and deep learning are also introduced. Prerequisites: Pass in STAT2602 and COMP2119 or same level. Proficiency in Python.

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.

At a time when liberal democracies are weakened by ideological polarization and the rise of populist movements challenging institutional checks and balances as well as the foundations of rational debate (Trumpism, the Bolsonaro episode, the AfD, etc.), it is becoming vital for future political, administrative, and academic leaders—who are often unfamiliar with scientific fundamentals, particularly in statistics—to acquire a basic grasp of such tools in order to define a framework for contributing to informed debate and evidence-based decision-making. This course provides them with that foundation through the lens of mathematical modeling. Concretely, it offers a rigorous methodology and a practical introduction to statistical modeling, taught through its logical application in structuring arguments and fostering debate. The objective is to equip students with practical tools that will allow them to analyze, interpret, and critically assess the use of data in their future professional environments, whether in strategy, economics, consulting, or public affairs management. With the help of AI-assisted applications, students learn to build, and interpret simple economic models, while developing a critical stance on the limitations and biases inherent in these models. The econometric article by Daron Acemoglu, recipient of the 2024 Nobel Prize, serves as one of the course's central threads, alongside more operational examples drawn from the corporate world and public sector. Through these applications, the course also offers students keys to understanding the mathematical foundations behind how artificial intelligence operates. The overarching ambition of this course is to enable students to become autonomous, clear-sighted, and critical actors in the use of data—capable of shaping the framework of public debate and decision-making at a time when perceptions of reality are increasingly influenced and polarized by the subjective interpretations of both populist opinion leaders and the prophets of artificial intelligence and big data.