Maria Cristina Mariani - Data Science in Theory and Practice

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DATA SCIENCE IN THEORY AND PRACTICE delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. The book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python and provides pseudo-algorithms to port the code to any other language. Ideal for students and practitioners without a strong background in data science, readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs,
will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.

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Data Science in Theory and Practice

Techniques for Big Data Analytics and Complex Data Sets

Maria Cristina Mariani University of Texas, El Paso El Paso, United States

Osei Kofi Tweneboah Ramapo College of New Jersey Mahwah, United States

Maria Pia Beccar-Varela University of Texas, El Paso El Paso, United States

This first edition first published 2022 2022 John Wiley and Sons Inc All - фото 16

This first edition first published 2022

© 2022 John Wiley and Sons, Inc.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions

The right of Maria Cristina Mariani, Osei Kofi Tweneboah, and Maria Pia Beccar‐Varela to be identified as the authors of this work has been asserted in accordance with law.

Registered Office John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA

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In view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

Library of Congress Cataloging‐in‐Publication Data applied for

ISBN: 9781119674689

Cover Design: Wiley

Cover Image: © nobeastsofierce/Shutterstock

List of Figures

Figure 4.1 Time series data of phase arrival times of an earthquake.

Figure 4.2 Time series data of financial returns corresponding to Bank of America (BAC) stock index.

Figure 4.3 Seasonal trend component.

Figure 4.4 Linear trend component. The horizontal axis is time картинка 17, and the vertical axis is the time series картинка 18. (a) Linear increasing trend. (b) Linear decreasing trend.

Figure 4.5 Nonlinear trend component. The horizontal axis is time картинка 19and the vertical axis is the time series картинка 20. (a) Nonlinear increasing trend. (b) Nonlinear decreasing trend.

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