Paula Brito is a Professor at the Faculty of Economics of the University of Porto, and a member of the Artificial Intelligence and Decision Support Research Group (LIAAD) of INESC TEC, Portugal. She holds a doctorate degree in Applied Mathematics from the University Paris Dauphine, and an Habilitation in Applied Mathematics from the University of Porto. Her current research focuses on the analysis of multidimensional complex data, known as symbolic data, for which she develops statistical approaches and multivariate analysis methodologies. In this context, she has been involved in two European research projects. Paula Brito has been president of the International Association for Statistical Computing (IASC-ISI) in 2013-2015, and of the Portuguese Association for Classification and Data Analysis for the term 2021-2023. She has been invited speaker at several international conferences, and is a regularly member of international program committees. Paula Brito has been chair of COMPSTAT 2008 and will co-chair the IFCS 2022 conference.

Sónia Dias is a Professor in the area of Mathematics at the School of Technology and Management of the Polytechnic Institute of Viana do Castelo, and a member of the Laboratory in Artificial Intelligence and Decision Support (LIAAD) of INESC TEC, Portugal. She holds a PhD in Applied Mathematics from the University of Porto (2014). Her main scientific areas of research are Data Analysis, Symbolic Data Analysis (analysis of multidimensional complex data) and Statistical/Mathematical Applications. Under this context, she has participated in several conferences and published articles in international journals and proceedings. She was a member of the organizing committee of the international Symbolic Data Analysis Workshop - SDA2018 and is a member of the organizing committee of the IFCS 2022 conference.

I Data Representation and Exploratory Analysis
1. Fundamental Concepts about Distributional Data
2. Descriptive Statistics based on Frequency Distributions
3. Descriptive Statistics for Numeric Distributional Data
4. The Quantile Methods to Analyze Distributional Data

II Clustering and Classification
5. Partitive and Hierarchical Clustering of Distributional Data using the Wasserstein Distance
6. Divisive clustering of histogram data
7. Clustering of Modal Valued Data
8. Mixture Models for Distributional Data
9. Classification of Continuous Distributional Data Using the Logratio Approach

III Dimension Reduction
10. Principal Component Analysis of Distributional Data
11. Principal Component Analysis of Numeric Distributional Data
12. Multidimensional Scaling of Distributional Data

IV Regression and Forecasting
13. Regression Analysis with the Distribution and Symmetric Distribution Model
14. Regression Analysis of Distributional Data Based on a Two-Component Model
15. Forecasting Distributional Time Series