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Principles of Data Science

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Cover of 'Principles of Data Science'

Table of Contents

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    Book Overview
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    Chapter 1 Simulation-Based Data Acquisition
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    Chapter 2 Coding of Bits for Entities by Means of Discrete Events (CBEDE): A Method of Compression and Transmission of Data
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    Chapter 3 Big Biomedical Data Engineering
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    Chapter 4 Big Data Preprocessing: An Application on Online Social Networks
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    Chapter 5 Feature Engineering
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    Chapter 6 Data Summarization Using Sampling Algorithms: Data Stream Case Study
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    Chapter 7 Fast Imputation: An Algorithmic Formalism
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    Chapter 8 A Scientific Perspective on Big Data in Earth Observation
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    Chapter 9 Visualizing High-Dimensional Data Using t-Distributed Stochastic Neighbor Embedding Algorithm
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    Chapter 10 Active and Machine Learning for Earth Observation Image Analysis with Traditional and Innovative Approaches
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    Chapter 11 Applications in Financial Industry: Use-Case for Fraud Management
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    Chapter 12 Stochastic Analysis for Short- and Long-Term Forecasting of Latin American Country Risk Indexes
Attention for Chapter 5: Feature Engineering
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Chapter title
Feature Engineering
Chapter number 5
Book title
Principles of Data Science
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-43981-1_5
Book ISBNs
978-3-03-043980-4, 978-3-03-043981-1
Authors

Sorin Soviany, Cristina Soviany

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