Anderson Alan - Statistics for Big Data for Dummies

Statistics For Big Data For Dummies

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Copyright 2015 by John Wiley & Sons, Inc., Hoboken, New Jersey

Published simultaneously in Canada

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Library of Congress Control Number: 2015943222

ISBN 978-1-118-94001-3 (pbk); ISBN 978-1-118-94002-0 (ePub); ISBN 978-1-118-94003-7 (ePDF)

1. Table of Contents

Welcome to Statistics For Big Data For Dummies! Every day, what has come to be known as big data is making its influence felt in our lives. Some of the most useful innovations of the past 20 years have been made possible by the advent of massive data-gathering capabilities combined with rapidly improving computer technology.

For example, of course, we have become accustomed to finding almost any information we need through the Internet. You can locate nearly anything under the sun immediately by using a search engine such as Google or DuckDuckGo. Finding information this way has become so commonplace that Google has slowly become a verb, as in I dont know where to find that restaurant Ill just Google it. Just think how much more efficient our lives have become as a result of search engines. But how does Google work? Google couldnt exist without the ability to process massive quantities of information at an extremely rapid speed, and its software has to be extremely efficient.

Another area that has changed our lives forever is e-commerce, of which the classic example is Amazon.com. People can buy virtually every product they use in their daily lives online (and have it delivered promptly, too). Often online prices are lower than in traditional brick-and-mortar stores, and the range of choices is wider. Online shopping also lets people find the best available items at the lowest possible prices.

Another huge advantage to online shopping is the ability of the sellers to provide reviews of products and recommendations for future purchases. Reviews from other shoppers can give extremely important information that isnt available from a simple product description provided by manufacturers. And recommendations for future purchases are a great way for consumers to find new products that they might not otherwise have known about. Recommendations are enabled by one application of big data the use of highly sophisticated programs that analyze shopping data and identify items that tend to be purchased by the same consumers.

Although online shopping is now second nature for many consumers, the reality is that e-commerce has only come into its own in the last 1520 years, largely thanks to the rise of big data. A website such as Amazon.com must process quantities of information that would have been unthinkably gigantic just a few years ago, and that processing must be done quickly and efficiently. Thanks to rapidly improving technology, many traditional retailers now also offer the option of making purchases online; failure to do so would put a retailer at a huge competitive disadvantage.

In addition to search engines and e-commerce, big data is making a major impact in a surprising number of other areas that affect our daily lives:

• Social media
• Online auction sites
• Insurance
• Healthcare
• Energy
• Political polling
• Weather forecasting
• Education
• Travel
• Finance

This book is intended as an overview of the field of big data, with a focus on the statistical methods used. It also provides a look at several key applications of big data. Big data is a broad topic; it includes quantitative subjects such as math, statistics, computer science, and data science. Big data also covers many applications, such as weather forecasting, financial modeling, political polling methods, and so forth.

Our intentions for this book specifically include the following:

• Provide an overview of the field of big data.
• Introduce many useful applications of big data.
• Show how data may be organized and checked for bad or missing information.
• Show how to handle outliers in a dataset.
• Explain how to identify assumptions that are made when analyzing data.
• Provide a detailed explanation of how data may be analyzed with graphical techniques.
• Cover several key univariate (involving only one variable) statistical techniques for analyzing data.
• Explain widely used multivariate (involving more than one variable) statistical techniques.
• Provide an overview of modeling techniques such as regression analysis.
• Explain the techniques that are commonly used to analyze time series data.
• Cover techniques used to forecast the future values of a dataset.
• Provide a brief overview of software packages and how they can be used to analyze statistical data.