Data science and social research : epistemology, Methods, technology and applications
Mentés helye:
| Testületi szerző: | |
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| Közreműködő(k): | |
| Különgyűjtemény: | e-book |
| Formátum: | könyv |
| Nyelv: | angol |
| Megjelenés: |
Cham : Springer International Publishing : Imprint: Springer,
2017
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| Sorozat: | Studies in classification, data analysis, and knowledge organization, ISSN 1431-8814 |
| Tárgyszavak: | |
| Online elérés: | http://dx.doi.org/10.1007/978-3-319-55477-8 |
| Címkék: |
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| 020 | |a 978-3-319-55477-8 | ||
| 024 | 7 | |a 10.1007/978-3-319-55477-8 |2 doi | |
| 040 | |a Springer |b hun |c ELTE | ||
| 041 | 0 | |a eng | |
| 245 | 0 | 0 | |a Data science and social research |b epistemology, Methods, technology and applications |c edited by N. Carlo Lauro, Enrica Amaturo, Maria Gabriella Grassia, Biagio Aragona, Marina Marino |
| 260 | |a Cham |b Springer International Publishing |b Imprint: Springer |c 2017 | ||
| 300 | |a IX, 300 p. |b ill., részben színes ; online forrás | ||
| 336 | |a szöveg |b txt |2 rdacontent | ||
| 337 | |a számítógépes |b c |2 rdamedia | ||
| 338 | |a távoli hozzáférés |b cr |2 rdacarrier | ||
| 347 | |a szövegfájl |b PDF |2 rda | ||
| 490 | 1 | |a Studies in classification, data analysis, and knowledge organization |x 1431-8814 | |
| 505 | 0 | |a Preface -- INDEX -- Introduction. Enrica Amaturo, Biagio Aragona -- Part I Epistemology: On Data, Big Data and Social Research. Is It a Real Revolution? Federico Neresini.-New Data Science - The Sociological Point of View; Biagio Aragona.- Data Revolutions in Sociology; Barbara Saracino.- Blurry Boundaries: Internet, Big New Data and Mixed-Method Approach; Enrica Amaturo, Gabriella Punziano.- Social Media and the Challenge of Big Data/Deep Data Approach; Giovanni Boccia Artieri.- Governing by Data - Some Considerations on the Role of Learning Analytics in Education; Rosanna De Rosa.- Part II Methods, Software and Data Architectures: A Knowledge-based Model for Clustering and Hierarchical Disjoint Non-negative Factor Analysis; Mario Fordellone, Maurizio Vichi.- TaLTaC 3.0. A Multi-levelWeb Platform for Textual Big Data in the Social Sciences; Sergio Bolasco and Giovanni De Gasperis.- Latent Growth and Statistical Literacy; Emma Zavarrone.- University of Bari’s Website Evaluation; Laura Antonucci, Marina Basile, Corrado Crocetta, Viviana D’Addosio, Francesco D. d’Ovidio, Domenico Viola.- Advantages of Administrative Data - Three Analyses of Students’ Careers in Higher Education; Andrea Amico, Giampiero D’Alessandro, Alessandra Decataldo.- Growth Curve Models to Detect Walking Impairment: the Case of InCHIANTI Study; Catia Monicolini, Carla Rampichini.- 1) Recurrence Analysis - Method and Applications; Maria Carmela Catone, Paolo Diana, Marisa Faggini.- Part III On-line Data Applications: Big Data and Network Analysis - A Promising Integration for Decision-Making; Giovanni Giuffrida, Simona Gozzo, Francesco Mazzeo Rinaldi, Venera Tomaselli.- White House Under Attack - Introducing Distributional Semantic Model for the Analysis of US Crisis Communication Strategies; Fabrizio Esposito, Estella Esposito, Pierpaolo Basile.- #theterrormood - Studying the World Mood after the Terror Attacks on Paris and Bru | |
| 520 | |a This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices. | ||
| 580 | |a Nyomtatott kiadás: ISBN 9783319554761 | ||
| 506 | |a Az e-könyvek a teljes ELTE IP-tartományon belül online elérhetők. | ||
| 598 | |a könyv | ||
| 595 | |a e-book | ||
| 500 | |a Olvasási képességet érintő fogyatékossággal élő személy számára (is) használható dokumentum. | ||
| 650 | 0 | 4 | |a információtudomány |
| 650 | 0 | 4 | |a társadalomtudományok |x kutatásmódszertan |
| 650 | 0 | 4 | |a társadalomtudományok |x információkutatás |
| 650 | 0 | 4 | |a információtechnológia |
| 650 | 0 | 4 | |a adattudomány |
| 650 | 0 | 4 | |a adatelemzés |
| 650 | 0 | 4 | |a big data |x informatika |
| 650 | 0 | |a Statistics. | |
| 650 | 0 | |a Epistemology. | |
| 650 | 0 | |a Social policy. | |
| 650 | 0 | |a Social sciences. | |
| 650 | 0 | |a Communication. | |
| 650 | 0 | |a Sociology. | |
| 650 | 0 | |a Mass media. | |
| 650 | 1 | 4 | |a Statistics. |
| 650 | 2 | 4 | |a Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. |
| 650 | 2 | 4 | |a Methodology of the Social Sciences. |
| 650 | 2 | 4 | |a Epistemology. |
| 650 | 2 | 4 | |a Social Policy. |
| 650 | 2 | 4 | |a Statistics and Computing/Statistics Programs. |
| 650 | 2 | 4 | |a Media Research. |
| 653 | |a tanulmányok | ||
| 653 | |a elektronikus könyv | ||
| 700 | 1 | |a Lauro, N. Carlo |e szerkesztő | |
| 700 | 1 | |a Amaturo, Enrica |e szerkesztő | |
| 700 | 1 | |a Grassia, Maria Gabriella |e szerkesztő | |
| 700 | 1 | |a Aragona, Biagio |e szerkesztő | |
| 700 | 1 | |a Marino, Marina |e szerkesztő | |
| 710 | 2 | |a SpringerLink (Online service) |e közreadó testület | |
| 830 | 0 | |a Studies in classification, data analysis, and knowledge organization | |
| 856 | 4 | 0 | |y Online változat |u http://dx.doi.org/10.1007/978-3-319-55477-8 |
| 850 | |a B2 | ||
| 264 | 1 | |a Cham |b Springer International Publishing |b Imprint: Springer |c 2017 | |