Principles of Data Science (Paperback, 1st ed. 2020)

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

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Transactions on Computational Science and Computational Intelligence
Release date:	July 2021
First published:	2020
Editors:	Hamid R Arabnia • Kevin Daimi • Robert Stahlbock • Cristina Soviany • Leonard Heilig • Kai Brussau
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	276
Edition:	1st ed. 2020
ISBN-13:	978-3-03-043983-5
Categories:	Books > Reference & Interdisciplinary > Communication studies > Data analysis Books > Computing & IT > Applications of computing > Pattern recognition Books > Business & Economics > Business & management > Business mathematics & systems > General Books > Professional & Technical > Electronics & communications engineering > Communications engineering / telecommunications > General Books > Computing & IT > Applications of computing > Databases > General
LSN:	3-03-043983-6
Barcode:	9783030439835

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