Principles of Data Science

✍ Scribed by Hamid R. Arabnia, Kevin Daimi, Robert Stahlbock, Cristina Soviany, Leonard Heilig, Kai Brüssau

• Publisher: Springer International Publishing;Springer
• Year: 2020
• Tongue: English
• Leaves: 289
• Series: Transactions on Computational Science and Computational Intelligence
• Edition: 1st ed.
• Category: Library

✦ Synopsis

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

✦ Table of Contents

Front Matter ....Pages i-xiv
Simulation-Based Data Acquisition (Fabian Lorig, Ingo J. Timm)....Pages 1-15
Coding of Bits for Entities by Means of Discrete Events (CBEDE): A Method of Compression and Transmission of Data (Reinaldo Padilha França, Yuzo Iano, Ana Carolina Borges Monteiro, Rangel Arthur)....Pages 17-30
Big Biomedical Data Engineering (Ripon Patgiri, Sabuzima Nayak)....Pages 31-48
Big Data Preprocessing: An Application on Online Social Networks (Androniki Sapountzi, Kostas E. Psannis)....Pages 49-78
Feature Engineering (Sorin Soviany, Cristina Soviany)....Pages 79-103
Data Summarization Using Sampling Algorithms: Data Stream Case Study (Rayane El Sibai, Jacques Bou Abdo, Yousra Chabchoub, Jacques Demerjian, Raja Chiky, Kablan Barbar)....Pages 105-124
Fast Imputation: An Algorithmic Formalism (Devisha Arunadevi Tiwari)....Pages 125-153
A Scientific Perspective on Big Data in Earth Observation (Corina Vaduva, Michele Iapaolo, Mihai Datcu)....Pages 155-188
Visualizing High-Dimensional Data Using t-Distributed Stochastic Neighbor Embedding Algorithm (Jayesh Soni, Nagarajan Prabakar, Himanshu Upadhyay)....Pages 189-206
Active and Machine Learning for Earth Observation Image Analysis with Traditional and Innovative Approaches (Corneliu Octavian Dumitru, Gottfried Schwarz, Gabriel Dax, Vlad Andrei, Dongyang Ao, Mihai Datcu)....Pages 207-231
Applications in Financial Industry: Use-Case for Fraud Management (Sorin Soviany, Cristina Soviany)....Pages 233-248
Stochastic Analysis for Short- and Long-Term Forecasting of Latin American Country Risk Indexes (Julián Pucheta, Gustavo Alasino, Carlos Salas, Martín Herrera, Cristian Rodriguez Rivero)....Pages 249-272
Correction to: Principles of Data Science (Hamid R. Arabnia, Kevin Daimi, Robert Stahlbock, Cristina Soviany, Leonard Heilig, Kai Brüssau)....Pages C1-C1
Back Matter ....Pages 273-278

✦ Subjects

Engineering; Communications Engineering, Networks; Computational Intelligence; Information Storage and Retrieval; Pattern Recognition; Big Data/Analytics