Hamid R. Arabnia & Kevin Daimi & Robert Stahlbock & Cristina Soviany & Leonard Heilig & Kai Brüssau
Language: English
Artificial Intelligence Business & Economics Business Mathematics Computer Vision & Pattern Recognition Computers Computers & Information Technology Electrical Engineering (General) Industries Optical Data Processing Storage & Retrieval System Administration Technology & Engineering Telecommunications
Publisher: Springer International Publishing
Published: Jul 9, 2020
Description:
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 expertsProvides a detailed explanation of data science perceptions, reinforced by practical examplesPresents a road map of future trends suitable for innovative data science research and practice