Principles of Data Science [electronic resource] /
Arabnia, Hamid R.editor.
Daimi, Kevin.editor.
Stahlbock, Robert.editor.
Soviany, Cristina.editor.
Heilig, Leonard.editor.
Brüssau, Kai.editor.
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.
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.
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.
Electrical engineering.
Computational intelligence.
Information storage and retrieval.
Pattern recognition.
Big data.
Communications Engineering, Networks.
Computational Intelligence.
Information Storage and Retrieval.
Pattern Recognition.
Big Data/Analytics.