The Big R-Book. Philippe J. S. De Brouwer

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      FROM DATA SCIENCE TO LEARNING MACHINES AND BIG DATA

       Philippe J.S. De Brouwer

      © 2021 John Wiley & 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 Philippe J.S. De Brouwer to be identified as the author of this work has been asserted in accordance with law.

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      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

      Names: De Brouwer, Philippe J. S., author.

      Title: The big R-book : from data science to learning machines and big data / Philippe J.S. De Brouwer.

      Description: Hoboken, NJ, USA : Wiley, 2020. | Includes bibliographical references and index.

      Identifiers: LCCN 2019057557 (print) | LCCN 2019057558 (ebook) | ISBN 9781119632726 (hardback) | ISBN 9781119632764 (adobe pdf) | ISBN 9781119632771 (epub)

      Subjects: LCSH: R (Computer program language)

      Classification: LCC QA76.73.R3 .D43 2020 (print) | LCC QA76.73.R3 (ebook) | DDC 005.13/3–dc23

      LC record available at https://lccn.loc.gov/2019057557

      LC ebook record available at https://lccn.loc.gov/2019057558

      Cover Design: Wiley

      Cover Images: Information Tide series and Particle Geometry series

      © agsandrew/Shutterstock, Abstract geometric landscape © gremlin/Getty Images, 3D illustration Rendering © MR.Cole_Photographer/Getty Images

      This book brings together skills and knowledge that can help to boost your career. It is an excellent tool for people working as database manager, data scientist, quant, modeller, statistician, analyst and more, who are knowledgeable about certain topics, but want to widen their horizon and understand what the others in this list do. A wider understanding means that we can do our job better and eventually open doors to new or enhanced careers.

      The student who graduated froma science, technology, engineering ormathematics or similar program will find that this book helps to make a successful step from the academic world into a any private or governmental company.

      This book uses the popular (and free) software R as leitmotif to build up essential programming proficiency, understand databases, collect data, wrangle data, buildmodels and select models froma suit of possibilities such linear regression, logistic regression, neural networks, decision trees, multi criteria decision models, etc. and ultimately evaluate a model and report on it.

      We will go the extra mile by explaining some essentials of accounting in order to build up to pricing of assets such as bonds, equities and options. This helps to deepen the understanding how a company functions, is useful to bemore result oriented in a private company, helps for one's own investments, and provides a good example of the theories mentioned before. We also spend time

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