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Statistics and Data Science: New Challenges, New Generations

The 2017 SIS (Italian Statistical Society) Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of "meaning" extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of "Big data", open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modeling, testing hypotheses and confirming conclusions drawn from the data.The Conference is also addressed to the new challenges of the "data analyst" where the traditional statistical topics are admitted with an extension to the related machine learning and computer science ones. A session on astrostatistics is planned.
When 28 June 2017 02:15 PM to
30 June 2017 02:15 PM
Where Florence IT
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  • Giorgio Alleva (President ISTAT) and Walter Radermacher (La Sapienza University of Rome)
  • Francesco Billari (Bocconi University and University of Oxford) 
  • Ian Dryden (University of Nottingham)
  • Fionn Murtagh (University of Derby, UK)



  • “Millennials” mobility patterns 
  • CLADAG: Clustering of high-dimensional data 
  • The population register and the integration with the household surveys 
  • Big data and official statistics 
  • Economic change and labour market 
  • Methods and applications for the treatment of Big Data in strategic fields 
  • Computational methods for high-dimensional and complex data sets 
  • AISP: Grabbing elusive populations and demographic behaviors by social media data 
  • Big Data Comes to School: Implications for Learning, Assessment, and Research 
  • Functional data with complex dependencies 
  • Shape, symbolic and object data 
  • GRASPA: Analysis of complex spatial data 
  • Data science for marketing and business 
  • SIS_Bayes: High-performance algorithms in Bayesian statistics 
  • Tensor-based methods for data science 
  • Advanced space-time models and functional analysis for seismic monitoring
  • Nowcasting@work 
  • S2G Combining big data with sample surveys
  • Big data analytics in banking and finance



  • The use of scanner data for multipurpose consumer price statistics 
  • New data collection: a tool for mobility and migration 
  • Financial data modelling
  • Statistical learning for complex data
  • Social research and data science: methods and applications 
  • Use of administrative databases for performance assessment in social contexts 
  • Neuroscience statistics 
  • Large dimensional dynamic factor models (estimation, shrinkage and model selection) 
  • Data science for network data 
  • Massive datasets in astrostatistics: theory & methods 
  • Social indicators and Big Data 



  • SASD – African Data Science Society 
  • SFdS – Société Française de Statistique (Group Data Mining et Apprentissage)
  • EuADS – European Association for Data Science
  • IASC – International Association for Statistical Computing
  • ENBIS – European Network for Business and Industrial Statistics

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