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PORTALE DELLA DIDATTICA

Data analytics for science and society

01SCVIU

A.A. 2018/19

Lingua dell'insegnamento

Inglese

Corsi di studio

Dottorato di ricerca in Ingegneria Informatica E Dei Sistemi - Torino

Organizzazione dell'insegnamento
Didattica Ore
Lezioni 15
Docenti
Docente Qualifica Settore h.Lez h.Es h.Lab h.Tut Anni incarico
Cerquitelli Tania Professore Associato ING-INF/05 6 0 0 0 2
Collaboratori
Espandi

Didattica
SSD CFU Attivita' formative Ambiti disciplinari
*** N/A ***    
2018/19
PERIODO: MARZO - APRILE 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 amount 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 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 PhD students learn by real analytics projects how to exploit data analytics technologies in their own research activities to mine useful knowledge. PhD 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 or application domains: -- Data Analytics for Science and Society -- Machine Learning for Pattern Recognition -- Mimetic Learning -- Text Mining and Analytics -- Visualization and Visual Analytics
PERIODO: MARZO - APRILE 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 amount 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 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 PhD students learn by real analytics projects how to exploit data analytics technologies in their own research activities to mine useful knowledge. PhD 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 or application domains: -- Data Analytics for Science and Society -- Machine Learning for Pattern Recognition -- Mimetic Learning -- Text Mining and Analytics -- Visualization and Visual Analytics
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 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) Questo corso illustra le diverse fasi del processo di data mining con particolare attenzione alle fasi della raccolta, integrazione, gestione e analisi dei dati. Attraverso una serie di studi di casi di riferimento, il corso illustra le criticità che emergono nelle fasi precedenti e le soluzioni tecnologicamente avanzate progettate e sviluppate per poterle affrontare. I progetti di analisi dei dati richiedono l’analisi di vaste collezioni di dati eterogenei. Con riferimento ai casi di studio considerati, nel corso saranno discusse le tecniche di integrazione dei dati e i paradigmi di rappresentazione dei dati per una rappresentazione unificata di collezioni di dati eterogenei che descriva tutte le caratteristiche del contesto considerato. Inoltre, nell’ambito dei casi di studio saranno analizzati i possibili utilizzi della vasta collezione di algoritmi di data mining, tra cui tecniche supervisionate, non supervisionate, ed esplorative. Esempi di casi di studio provenienti dai seguenti domini saranno esaminati nel corso: - applicazioni mediche (ad esempio, analisi di dati clinici e fisiologici) - applicazioni energetiche - social networks (ad esempio, analisi dei dati tweet) - applicazioni in contesto urbano
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 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) Questo corso illustra le diverse fasi del processo di data mining con particolare attenzione alle fasi della raccolta, integrazione, gestione e analisi dei dati. Attraverso una serie di studi di casi di riferimento, il corso illustra le criticità che emergono nelle fasi precedenti e le soluzioni tecnologicamente avanzate progettate e sviluppate per poterle affrontare. I progetti di analisi dei dati richiedono l’analisi di vaste collezioni di dati eterogenei. Con riferimento ai casi di studio considerati, nel corso saranno discusse le tecniche di integrazione dei dati e i paradigmi di rappresentazione dei dati per una rappresentazione unificata di collezioni di dati eterogenei che descriva tutte le caratteristiche del contesto considerato. Inoltre, nell’ambito dei casi di studio saranno analizzati i possibili utilizzi della vasta collezione di algoritmi di data mining, tra cui tecniche supervisionate, non supervisionate, ed esplorative. Esempi di casi di studio provenienti dai seguenti domini saranno esaminati nel corso: - applicazioni mediche (ad esempio, analisi di dati clinici e fisiologici) - applicazioni energetiche - social networks (ad esempio, analisi dei dati tweet) - applicazioni in contesto urbano
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