Christopher Greco - Data Science Tools: R, Excel, KNIME, & OpenOffice

DATA SCIENCE
TOOLS

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DATA SCIENCE
TOOLS

R, Excel, KNIME, & OpenOffice

CHRISTOPHER GRECO

MERCURY LEARNING AND INFORMATION

Dulles, Virginia

Boston, Massachusetts
New Delhi

Copyright 2020 by MERCURY LEARNING AND INFORMATION LLC. All rights reserved.

This publication, portions of it, or any accompanying software may not be reproduced in any way, stored in a retrieval system of any type, or transmitted by any means, media, electronic display or mechanical display, including, but not limited to, photocopy, recording, Internet postings, or scanning, without prior permission in writing from the publisher.

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C. Greco. Data Science Tools: R, Excel, KNIME, & OpenOffice.
ISBN: 978-1-68392-583-5

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Library of Congress Control Number: 2020937123

202122321 Printed on acid-free paper in the United States of America

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CONTENTS

PREFACE

Data Science is all the rage. There is a great probability that every book you read, every Web site that you visit, every advertisement that you receive, is a result of data science and, with it, data analytics. What used to be statistics is now referenced as data analytics or data science. The concepts behind data science are myriad and complex, but the underlying concept is that very basic statistical concepts are vital to understanding data. This book really has a two-fold purpose. The first is to review briefly some of the concepts that the reader may have encountered while taking a course (or courses) in statistics, while the second is to demonstrate how to use tools to visualize those statistical concepts.

There are several caveats that must accompany this book. The first one is that the tools are of a certain version, which will be described below. This means that there will undoubtedly be future versions of these tools that might perform differently on your computer. I want to be very clear that this performance does not mean that these tools will perform better. Three of these are free and open source tools, and, as such, perform as well as the group of developers dictate they will in their most current versions. In most instances, the tool will be enhanced in the newer version, but there might be a different buttonology that will be associated with newer functions. You will see the word buttonology throughout this book in the form of the mechanics of the tool itself. I am not here to teach the reader statistics or the different concepts that compose the topics of this book. I am here to show you how the free and open source tools are applied to these concepts.

Now it is time to get to the very heart of the text, the tools of data science. There will be four tools that will encompass the content of this book. Three are open source tools (FOSS or Free and Open Source), with one being COS (Common Off the Shelf) software, but all four will require some instruction in their use. These are not always intuitive or self-explanatory, so there will be many screen pages for each mechanical function. I feel that visual familiarization trumps narrative, so you will not see a lot of writing, mostly descriptions and step-by-step mechanics. A few of you may be wondering how to practice these skills, and for those readers there is a final chapter that has several scenarios that allow the reader to apply what they have learned from these tools.

The organization of this book will be on the statistical concept, not the tool, which means that each chapter will encompass an explanation of the statistical concept, and then how to apply each tool to that concept. By using this presentation method, readers can go to the prescribed concept and use the tool most comfortably applied. Each section will be labeled accordingly, so they will both be in the table of contents and the index. This makes it simpler for individuals to see their choice of tools and the concepts they have to apply to those tools.

C. Greco
April 2020

ACKNOWLEDGMENTS