The use of Information and Communication Technologies has currently made available a huge amount of heterogeneous data in various real application domains (e.g., in the domain of urban computing, medical analytics, behavioral analytics). As deeply digging these large amounts of collected data can unearth a rich spectrum of knowledge in the targeted domain, there is a great need to increase data-analytics capability in various communities. However, data analytics on real data collections is still a daunting task, because various challenges about data science arise dealing with the collection, storage, search, sharing, modeling, analysis, and visualization of data, information, and knowledge. Furthermore, since analytics algorithms are powerful and necessary tools behind a large part of the information we use every day, rendering them more transparent should improve their usability in various areas. The course discusses a sample of real analytics case studies to illustrate how to design, develop, and test a data analytics project. This will be done through discussing critical issues that arise in a data analytics project and presenting the latest advancements in the area of analytics applications with a special focus on how cutting-edge solutions have been designed and developed to gain useful and interpretable insights. An overview of the data analytics solutions and technologies will be thoroughly discussed together with real-life use cases in which these approaches are essential. The objective of this course is that Ph.D. students learn by real analytics projects on how to exploit data analytics technologies in their own research activities to mine useful knowledge. Ph.D. students can understand the different phases in which the data analytics project is structured, which are the critical issues that arise, and how they can be addressed. This course belongs to an educational path on Data Science. The path is composed by - an introductory course (Data Mining: Concepts and Algorithms), covering data analytics fundamentals, which is a cultural prerequisite for the other courses - 5 thematic courses dealing in-depth with specific Data Science topics, such as different algorithm types of application domains: -- Data Analytics for Science and Society -- Machine Learning for Pattern Recognition -- Mimetic Learning -- Text Mining and Analytics -- Visualization and Visual Analytics technologies in their own research activities to mine useful knowledge. Ph.D. students can understand the different phases in which the data analytics project is structured, which are the critical issues that arise, and how they can be addressed. This course belongs to an educational path on Data Science. The path is composed by - an introductory course (Data Mining: Concepts and Algorithms), covering data analytics fundamentals, which is a cultural prerequisite for the other courses - 5 thematic courses dealing in-depth with specific Data Science topics, such as different algorithm types of application domains: -- Data Analytics for Science and Society -- Machine Learning for Pattern Recognition -- Mimetic Learning -- Text Mining and Analytics -- Visualization and Visual Analytics

Data mining: basics concepts

This course illustrates the different phases of the data mining process with particular emphasis on the phases of data collection, data fusion, data management, and data analytics. Through a set of reference case studies, the course discusses the critical issues arising in the above phases and the cutting-edge solutions that have been designed and developed to address them. Data analytics projects usually require to deal with a large amount of heterogeneous data collections. Data fusion techniques and data representation paradigms are discussed to integrate the heterogeneous collected data into a unified representation describing all facets of the targeted domain. Moreover, the exploitation of the vast collection of data mining algorithms in the considered cases, including supervised, unsupervised, and exploratory data mining techniques, is discussed. Case studies coming from the following real-life analytics domains will be investigated in the course: - Medical domain (e.g., mining clinical and physiological data) - Energy informatics - Social dynamics (e.g., tweet data analysis) - Urban computing (e.g., mining urban data)

On line synchronous mode

Laborartory test on experimental practice or informatics

P.D.2-2 - June