The purpose of the course is to provide the students with the knowledge of the fundamentals of statistics and statistical learning for data analysis, starting from the vocabulary of statistics to model implementation and interpretation. The course is intended to have both theoretical mathematical justification of statistical analysis and practical application to real world problems. The introduction of theoretical concepts is always combined with the application of statistical methods to analyze real-world data and solve practical problems in a variety of domains including management, marketing, economics and finance. Therefore, at the end of the course the students will be able to: - being able to transform a real world problems into a statistical language problem - understand the main domains of applications of statistics, with particular reference to the areas of management, economics, marketing and finance - understand the main concepts of theoretical and applied statistics - - model real-world data, including corporate and market data - reasonably interpret model outputs and derive implications for the specific domain of knowledge - formulate and build predictive models, forecast key variables and assess forecast uncertainty
Prerequisiti
none
Metodi didattici
The course is structured in theoretical and practical lectures with the R software. Both theoretical and practical lectures are based on the instructor’s material. Students may also wish to refer to the following books for: a) a basic statistics review - Newbold, P. (2013). Statistics for business and economics. Pearson;
Verifica Apprendimento
Final written examination
Contenuti
Lectures contents: Introduction to Statistics, its basic vocabulary and gergo. Elements of qualitative and quantitative descriptive statistics (univariate). Elements of qualitative and quantitative descriptive statistics (bivariate and multivariate). Elements of inferential statistics for one and two sample. Simple and Multiple linear regression model. Logistic regression model. Introduction to supervised and unsupervised learning / Networks - decision trees and random forests, K-means and hierarchical clustering,introduction to network science Final recap of the illustrated statistical methods and their joint discussion.
