國立虎尾科技大學 |

Principles of Data Science

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Principles of Data Science/ edited by Hamid R. Arabnia, Kevin Daimi, Robert Stahlbock, Cristina Soviany, Leonard Heilig, Kai Brüssau.
其他作者:	Arabnia, Hamid R.
面頁冊數:	XIV, 276 p. 102 illus., 55 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Electrical engineering. -
電子資源:	https://doi.org/10.1007/978-3-030-43981-1
ISBN:	9783030439811

Principles of Data Science
Principles of Data Science[electronic resource] /edited by Hamid R. Arabnia, Kevin Daimi, Robert Stahlbock, Cristina Soviany, Leonard Heilig, Kai Brüssau. - 1st ed. 2020. - XIV, 276 p. 102 illus., 55 illus. in color.online resource. - Transactions on Computational Science and Computational Intelligence,2569-7072. - Transactions on Computational Science and Computational Intelligence,.

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.

ISBN: 9783030439811

Standard No.: 10.1007/978-3-030-43981-1doiSubjects--Topical Terms:

596380
Electrical engineering.

LC Class. No.: TK1-9971

Dewey Class. No.: 621.382

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