This book focuses on methods and models in classification and data analysis and presents real-world applications at the interface with data science. Numerous topics are covered, ranging from statistical inference and modelling to clustering and factorial methods, and from directional data analysis to time series analysis and small area estimation. The applications deal with new developments in a variety of fields, including medicine, finance, engineering, marketing, and cyber risk. The contents comprise selected and peer-reviewed contributions presented at the 13th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2021, held (online) in Florence, Italy, on September 9–11, 2021. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results at the interface between classification and data science. This book focuses on methods and models in classification and data analysis and presents real-world applications at the interface with data science. Numerous topics are covered, ranging from statistical inference and modelling to clustering and factorial methods, and from directional data analysis to time series analysis and small area estimation. The applications deal with new developments in a variety of fields, including medicine, finance, engineering, marketing, and cyber risk. The contents comprise selected and peer-reviewed contributions presented at the 13th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2021, held (online) in Florence, Italy, on September 9–11, 2021. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results at the interface between classification and data science. Leonardo Grilli is a Full Professor of Statistics at the University of Florence, Italy and the Director of the Master’s program in Statistics and Data Science and a member of the board of the Doctoral Program in Development Economics and Local Systems. His teaching activity focuses on introductory statistics and statistical modelling, including generalized linear models and multilevel models. His research mainly concerns random effects models for multilevel analysis, with methodological advances about the specification and estimation of models in complex frameworks such as duration data, multivariate qualitative responses, informative sampling designs, and sample selection bias. He has also made contributions to causal inference in the potential outcomes framework, IRT models, latent growth curve models, mixture models, and quantile regression. Monia Lupparelli is an Associate Professor of Statistics at the University of Florence, Italy, and a member of the boardof the Doctoral Program in Statistics at the University of Bologna. She teaches the foundations of statistics and of statistical modelling in bachelor’s and master’s degree programs. She also gives specialised courses on graphical models and categorical data analysis for doctoral and post-doctoral students. Her research activity is mainly grounded in network modelling, latent variable models, and marginal models with applications to the analysis of longitudinal and categorical data in social and biomedical sciences. Carla Rampichini is a Full Professor of Statistics and Head of the Department of Statistics, Computer science, and Applications at the University of Florence, Italy. Her teaching activity focuses on introductory statistics, multivariate analysis and statistical modelling. Her research interests relate to random effects models for multilevel analysis, program evaluation, and causal inference. Her methodological work is joined with applications often concerning the effectiveness of universities. She is a member of the Royal Statistical Society and the Italian Statistical Society. She is the Editor-in-Chief of Statistical Methods and Applications. Emilia Rocco is an Associate Professor of Statistics at the University of Florence, Italy. Her teaching activity focuses on introductory statistics and survey sampling theory and applications. Her research interests relate to informative and not-informative sample designs useful for studying rare and clustered populations, capture-recapture methodologies, methods of treatment of nonresponse, indicators of representativeness for monitoring the risk of selection bias when non-response or a convenience sample occurs, small area estimation methods, geoadditive models and their applications to the analysis of environmental and socio-economic phenomena. She is a member o