Professors involved: Dr.ssa Barbara Tarantino, Dr Davide Sacco and Prof Davide Gentilini

Course learning outcomes/abstract: by the end of the course, participants will be able to: Demonstrate operational proficiency in R, including the use of core data structures and essential programming constructs. Import, manage and transform datasets of different formats using appropriate R functions and workflows. Perform fundamental statistical analyses, interpret outputs correctly, and evaluate the assumptions behind common statistical methods. Produce clear and informative visualizations using both base R and modern graphical packages such as ggplot2. Apply reproducible analytical practices, including script organization, documentation, and structured coding approaches. Implement complete analytical pipelines, from raw data to summary statistics, visual output and basic modelling.

Goals: provide participants with a solid introduction to R as a computational environment for data analysis. Build foundational skills necessary for handling, processing and understanding empirical datasets. Develop the ability to choose appropriate statistical tools for different research questions. Promote good practices in reproducibility, transparency and coding ethics within quantitative research. Empower early-stage researchers to approach data-driven problems independently and confidently. Encourage methodological awareness and critical thinking in the interpretation of quantitative results.

Number of hours and planning: 20 hours

Period: June 22-23-24-25-26 2026

Registration: https://forms.gle/UmXK6DMswL7ZdmsZ6

Delivery mode and location: on line

Language: English

Evaluation criterial: the degree of learning will be tested at the end of the course by subjecting the participants to a test. The verification test will consist of a dataset to analyze and a series of questions related to the dataset to answer

Credits (CFU): 5

Depliant