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Principles of Data Science [electronic resource] / edited by Hamid R. Arabnia, Kevin Daimi, Robert Stahlbock, Cristina Soviany, Leonard Heilig, Kai Brüssau.

Contributor(s): Arabnia, Hamid R [editor.] | Daimi, Kevin [editor.] | Stahlbock, Robert [editor.] | Soviany, Cristina [editor.] | Heilig, Leonard [editor.] | Brüssau, Kai [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Transactions on Computational Science and Computational Intelligence: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: XIV, 276 p. 102 illus., 55 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030439811.Subject(s): Telecommunication | Computational intelligence | Information storage and retrieval systems | Pattern recognition systems | Quantitative research | Communications Engineering, Networks | Computational Intelligence | Information Storage and Retrieval | Automated Pattern Recognition | Data Analysis and Big DataAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
Contents:
Introduction -- Data Acquisition, Extraction, and Cleaning -- Data Summarization and Modeling -- Data Analysis and Communication Techniques -- Data Science Tools -- Deep Learning in Data Science -- Data Science Applications -- Conclusion.
In: Springer Nature eBookSummary: This book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists’ preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science. Introduces various techniques, methods, and algorithms adopted by Data Science experts Provides a detailed explanation of data science perceptions, reinforced by practical examples Presents a road map of future trends suitable for innovative data science research and practice.
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Introduction -- Data Acquisition, Extraction, and Cleaning -- Data Summarization and Modeling -- Data Analysis and Communication Techniques -- Data Science Tools -- Deep Learning in Data Science -- Data Science Applications -- Conclusion.

This book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists’ preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science. Introduces various techniques, methods, and algorithms adopted by Data Science experts Provides a detailed explanation of data science perceptions, reinforced by practical examples Presents a road map of future trends suitable for innovative data science research and practice.

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