Data Mining

Provided by: 



M.Sc. in Data Science and BI


Polo Fibonacci, University of Pisa






Educational Goals: 

DATA MINING: FOUNDATIONS The formidable advances in computing power, data acquisition, data storage and connectivity have created unprecedented amounts of data. Data mining, i.e., the science of extracting knowledge from these masses of data, has therefore been affirmed as an interdisciplinary branch of computer science. Data mining techniques have been applied to many industrial, scientific, and social problems, and are believed to have an ever deeper impact on society. The course objective is to provide an introduction to the basic concepts of data mining and the process of extracting knowledge, with insights into analytical models and the most common algorithms. DATA MINING: ADVANCED ASPECTS AND APPLICATIONS The second part of the course completes the knowledge of the first module with: a review of advanced techniques for the mining of new forms of data; a review of the main application areas and case studies.


DATA MINING: FOUNDATIONS Fundamentals of data mining and the knowledge discovery process Explorative Data Analysis and Visual analytics Frequent patterns and Rules Clustering: partition based techniques, density based techniques and hierarchical techniques Classification: decision trees and Bayesian Methods Analytical experiments with data mining tools DATA MINING: advanced aspects and applications Mining of time series and spatio-temporal data Mining of sequential data and graphs Advanced techniques for classification, clustering and outlier detection Language, standard and architectures of data mining systems Social impact of data mining Data mining and privacy protection Case studies in socio-economic domains (marketing and CRM, mobility and transport, public health, etc.)
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