The SIS group Statistics and Data Science aims at fostering the statisticsal skills in the scientific research and teaching in the context of Data Science. Its goals are:

• to constitute a reference within the SIS on Data Science themes with the aim of establishing contacts, interactions, and systematic relationships with other national and international statistical societies and with other academic and non-academic communities interested in Data Science from different perspectives;

• to promote and coordinate, through research projects and workshops, theoretical and applied research with emphasis on the role of Statistics in Data Science;

• to promote contributions and specialized sessions on Data Science topics within the institutional events of SIS;

• to support those who promote, coordinate, or participate in the development of Data Science training programs at any level and in particular in the universities and to promote the organization of courses, schools, or tutorials within workshops;

• to promote the dissemination of the scientific activities of its members, and of work and research opportunities in Data Science.

Conference topics

Researchers and practitioners interested in Statistics and Data Science and the related methodological and applied fields are invited to submit papers for the Third Meeting of the Statistics and Data Science group. The interplay between Statistics and Data Science is gaining more and more interest not only for statisticians but also for Physicists and Computer Scientists. We call for papers treating themes related to the modelling and analysis of complex data and proposing new or ad hoc approaches pertaining to the following aims:

Computational Statistics
Data Ethics & Fairness in Machine Learning Data Stream Analysis
Data Visualization
Deep Learning
Dimensional Reduction
Education & Data Literacy
Ensemble methods for classification Explainable AI
Geometrical Data Analysis
Learning with Imbalanced Data
Mining Complex Data
Practice & Applications
Prediction & Classification
Preference Learning
Textual Data Analysis
Unsupervised Models
Variational Inference

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