Course Topics
M1: - Review of statistical inference: random variables, confidence intervals, and hypothesis testing. - Introduction to statistical learning concepts: basic vocabulary and notions, parametric and nonparametric approaches, predictive and inferential objectives, bias-variance trade off, supervised and unsupervised learning - Linear regression and extensions: simple and multiple linear regression, model estimation and assessment, model assumptions, inferential tools, qualitative predictors, interaction effects, polynomial regression, basic notions on nonparametric regression - Classification: introduction to classification, logistic regression, model estimation, evaluation of classifiers - Other supervised learning techniques: trees, splines, additive models - Model selection/assessment and evaluation of model complexity: resampling methods, cross-validation and information criteria - Unsupervised learning: clustering tools such as k-means and hierarchical clustering, principal component analysis - Applications with the R software M2: - Parameter estimation: maximum likelihood methods - Parameter estimation: Bayesian inference - Time series: components and forecasting - Time series: causal relationship tests - Missing data - Elements of statistics for Big Data

Teaching format
M1: In-person lectures and computer labs. Whenever possible, lectures will be structured to prioritize in-class time for discussions, and practical applications. M2: Frontal lectures, discussions and exercises on computer.

Educational objectives
Knowledge and understanding: The student will acquire knowledge of the analytical techniques and tools required to understand and quantitatively analyse economic and business phenomena in order to support decision-making processes. Knowledge of statistical inference, linear models and their generalisations, linear algebra, and optimisation techniques will be consolidated. In-depth knowledge of the main techniques of supervised and unsupervised statistical learning will be acquired, which are functional for the development of analysis and visualisation capabilities of economic and business data. Applying knowledge and understanding: Ability to apply and implement analysis techniques focusing on different types of datasets such as streaming data, tabular data, documents and images and analysis on joint datasets. Ability to apply supervised and unsupervised learning topics, and knowledge modelling, extraction, integration, analysis and exploitation; these skills are declined in various application domains of interest to companies and public and private entities Making judgements: Master graduates will have the ability to apply the acquired knowledge to interpret data in order to make managerial and operational decisions in a business context. Master's graduates will have the ability to apply the acquired knowledge to support processes related to production, management and risk promotion activities and investment choices through the organisation, analysis and interpretation of complex databases. Communication skills: Master's graduates will be able to communicate effectively in oral and written form the specialised contents of the individual disciplines, using different registers, depending on the recipients and the communicative and didactic purposes, and to evaluate the formative effects of their communication. Learning skills: Graduates will be familiar with the tools of scientific research. They will also be able to make autonomous use of information technologies to carry out bibliographic research and investigations both for their own training and for further education. In addition, through the curricular teaching and the activities related to the preparation of the final thesis, they will be able to acquire the ability - to identify thematic links and to establish relationships between methods of analysis and application contexts; - to frame a new problem in a systematic manner and to implement appropriate analysis solutions; - to formulate general statistical-econometric models from the phenomena studied.

Assessment
The overall exam mark will be determined by the assessment of the two modules (M1+M2). M1: Assessment (for both attending and non-attending students): - Written Exam: Exercises and review questions (65% of the final grade). - Data Analysis Project: Group project in which students select and analyze an interesting dataset using the tools learned in the course. Groups will present their work at the end of the course (35% of the final grade; optional). Notes: - For students who do not complete the project, the written exam will count for 100% of the final grade. - Project grades remain valid for one academic year. M2: The assessment is based on class and lab participation, home-work exercises and a final written exam. The final written exam will include open questions and exercises to be worked out by the students as well as computational exercises to be solved with R.

Evaluation criteria
M1: - Written exam: understanding of statistical concepts, correct interpretation of results of statistical analyses, clarity and precision of explanations. - Data Analysis Project: Quality and clarity of the presentation, adequacy and appropriateness of analyses with respect to dataset characteristics M2: For attending students the final grade will be determined by the evaluation of homeworks, class and lab participation (20%) and the evaluation of a final written exam (80%). The homeworks and the final written exam are separately evaluated with a score expressed in 30/30. For non-attending students the final grade will be determined by the evaluation of a final written exam (100%). The final written exam is evaluated with a score expressed in 30/30.