Principles of data science / Hamid R. Arabnia, Kevin Daimi, Robert Stahlbock, Cristina Soviany, Leonard Heilig, Kai Brüssau, editors.
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 scien...
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| Other Authors: | , , , , , |
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| Format: | Ebook |
| Language: | English |
| Published: |
Cham :
Springer,
2020.
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| Series: | Transactions on computational science and computational intelligence.
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| Subjects: | |
| Online Access: | Springer eBooks |
| Summary: | 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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| Item Description: | Includes index. |
| Physical Description: | 1 online resource. |
| ISBN: | 3030439801 9783030439804 303043981X 9783030439811 |
| ISSN: | 2569-7072 |