Soft Methods for Data Science [electronic resource] /
Contributor(s): Ferraro, Maria Brigida [editor.] | Giordani, Paolo [editor.] | Vantaggi, Barbara [editor.] | Gagolewski, Marek [editor.] | Ángeles Gil, María [editor.] | Grzegorzewski, Przemysław [editor.] | Hryniewicz, Olgierd [editor.] | SpringerLink (Online service).
Series: Advances in Intelligent Systems and Computing: 456Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: XVI, 535 p. 70 illus., 30 illus. in color. | Binding - Card Paper |.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319429724.Subject(s): Computer Engineering
| Probability Theory and Stochastic Processes | Probability and Statistics in Computer ScienceDDC classification: 006.3 Online resources: Click here to access eBook in Springer Nature platform. (Within Campus only.)
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Springer Nature eBookSummary: This proceedings volume is a collection of peer reviewed papers presented at the 8th International Conference on Soft Methods in Probability and Statistics (SMPS 2016) held in Rome (Italy). The book is dedicated to Data science which aims at developing automated methods to analyze massive amounts of data and to extract knowledge from them. It shows how Data science employs various programming techniques and methods of data wrangling, data visualization, machine learning, probability and statistics. The soft methods proposed in this volume represent a collection of tools in these fields that can also be useful for data science.
This proceedings volume is a collection of peer reviewed papers presented at the 8th International Conference on Soft Methods in Probability and Statistics (SMPS 2016) held in Rome (Italy). The book is dedicated to Data science which aims at developing automated methods to analyze massive amounts of data and to extract knowledge from them. It shows how Data science employs various programming techniques and methods of data wrangling, data visualization, machine learning, probability and statistics. The soft methods proposed in this volume represent a collection of tools in these fields that can also be useful for data science.
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