The course aims to teach students the main statistical quantitative methods for data analysis through the introduction of measures, models, and techniques of descriptive and inferential statistics. Specifically, notions of descriptive statistics will enable students to conduct basic exploratory analyses, while inferential statistics will provide the methodological foundation for analyzing data under conditions of uncertainty. With the acquired knowledge, students will be able to perform exploratory analysis and apply the main statistical techniques to real-world data.

Referring to the Dublin Descriptors, the teaching objectives are outlined as follows:

Knowledge and understanding: by the end of the course, students will gain specific knowledge on statistical analysis methodologies to observe, describe, analyze, and interpret real-world phenomena using fundamental statistical tools. The course aims to provide students with the methodological knowledge to perform univariate, bivariate descriptive analyses, and inferential analyses, that is under conditions of uncertainty.

Applying knowledge and understanding: by the end of the course, students will have developed solid methodological knowledge and analytical skills. Consequently, students will be able to independently conduct empirical data analyses, evaluate their results, recognize the suitability of the adopted methodology, and identify potential limitations.

Making judgments: The course aims to support a critical approach to the use of different statistical techniques for interpreting real-world phenomena. In particular, students will develop critical skills regarding the use of various methods depending on the analysis objectives of the studied phenomenon.

Communication skills: Throughout the course, students will acquire specific skills to effectively communicate descriptive or inferential analysis methodologies related to real-world phenomena. This entails not only the ability to understand and apply these methodologies but also to clearly communicate them to the others. This involves explaining the techniques used to analyze data, interpreting results meaningfully, and presenting them clearly and comprehensibly to enable an effective communication of conclusions derived from the analysis of real data.

Learning skills: The adopted teaching methodologies involve continuous assessment of students learning. This approach aims to enhance students independent judgments and self-learning skills. Students are encouraged to develop critical evaluation capabilities.