Learning Objectives
The course aims to provide students with the main statistical and quantitative methods for performing data analysis through the introduction of measures, models, and techniques of descriptive and inferential statistics. In particular, concepts of descriptive statistics will enable students to carry out basic exploratory analyses, while concepts of inferential statistics will provide the methodological foundation for performing analyses under conditions of uncertainty. By applying the knowledge acquired during the course, students will be able to conduct exploratory data analyses and apply the main statistical processing techniques to data describing real-world phenomena.
Knowledge and understanding
By the end of the course, students will have acquired specific knowledge of statistical analysis methodologies for observing, describing, analyzing, and interpreting real phenomena using the basic tools of statistics. The purpose of the course is to provide students with the methodological knowledge required to perform univariate and bivariate descriptive analyses, as well as inferential analyses under uncertainty.
Applying knowledge and understanding
By the end of the course, students will have developed a solid methodological foundation and analytical skills. They will be able to independently carry out empirical data analyses, assess the results, evaluate the adequacy of the applied methodology, and identify its potential limitations.
Making judgments
The course is designed to foster a critical approach to the use of different statistical techniques for the interpretation of real-world phenomena. Students will develop the ability to critically evaluate and select appropriate statistical methods according to the specific objectives of their analysis.
Communication skills
Throughout the course, students will acquire the necessary skills to effectively communicate statistical methodologies (both descriptive and inferential) applied to real phenomena. This involves not only understanding and applying these methodologies but also clearly communicating them to others — explaining the analytical techniques used, interpreting the results meaningfully, and presenting them clearly and comprehensibly to ensure effective communication of conclusions derived from data analysis.
Learning skills
The teaching methods used in the course include continuous assessment of students’ learning progress. This approach is intended to enhance students’ autonomy in judgment and self-learning abilities, encouraging them to develop critical evaluation skills and greater independence in applying statistical reasoning.
- Docente: francesco cappelli