R语言实战：企业数据分析实用指南

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"《业务分析实战：R语言指南》是一本由Dr. Umesh R. Hodeghatta和Umesha Nayak合著的实用教程，针对希望理解和学习R在业务分析中的基础知识的初学者。该书出版于2017年2月5日，以英文撰写，共266页，提供PDF格式，大小约为14.52 MB。本书的核心内容围绕如何利用R语言进行业务统计、数据挖掘和机器学习，帮助读者掌握处理组织内大量数据所需的基本技能。作者通过实例讲解，详细阐述了正确实施业务分析的步骤，并强调在何种情境下特定技术可能不适用，以便读者理解其实际应用的边界。书中涵盖了以下关键主题： 1. R语言入门：介绍数据管理的基础知识以及R语言的各种函数，使读者能够编写程序来处理数据。 2. 业务分析项目架构：探讨项目的生命循环，包括其设计、执行和评估过程。 3. 描述性分析：运用R进行描述性统计并进行数据清洗，为深入分析做好准备。 4. 数据挖掘：讲解分类、关联规则和聚类等技术，展示如何通过R发现数据中的模式和规律。 5. 预测分析：涵盖简单回归、多元回归和逻辑回归等方法，教授如何进行预测模型的构建。书中的案例研究特别关注了诸如分类、关联分析、聚类和回归等重要的业务分析技术，通过R语言的实际操作演示概念。通过阅读本书，读者不仅能编写分析程序，还能构建分析模型并从中提取有价值的信息，理解数据挖掘和机器学习的基本概念，甚至学会如何将商业问题转化为可分析的问题。《业务分析实战：R语言指南》适合学生、管理者、战略规划专业人士、软件开发人员以及BI/DW（商务智能/数据仓库）领域的专业人士，旨在提供一个实践导向的学习路径，帮助他们在这个快速发展的数据分析领域取得成功。版权信息和版权声明清晰明确，确保读者能够在合法范围内使用和传播书中的内容。"

xvii

About the Technical

Reviewer

Jojo Moolayil is a data scientist and author of Smarter Decisions—The Intersection of

Internet of Things and Decision Science. With over four years of industrial experience in

data science, decision science, and IoT, he has worked with industry leaders on high-

impact and critical projects across multiple verticals. He is currently associated with

General Electric, a pioneer and leader in data science for industrial IoT, and lives in

Bengaluru—the Silicon Valley of India

He was born and raised in Pune, India and graduated from the University of Pune

with a major in information technology engineering. He started his career with Mu Sigma,

the world’s largest pure play analytics provider, and worked with the leaders of many

Fortune 50 clients. One of the early enthusiasts to venture into IoT analytics, he now

focuses on solving decision science problems for industrial IoT use cases. As a part of

his role at GE, he also develops data science and decision science products and platforms

for industrial IoT. Jojo is also an active data science tutor and maintains a blog at

www.jojomoolayil.com/web/blog/.

Chapter 1 ■ Overview Of Business analytiCs

• You enter a grocery store and you find that your regular monthly

purchases are already selected and set aside for you. The only

decision you have to make is whether you require all of them or

want to remove some from the list. How happy you are!

• Your preferred airline reserves tickets for you well in advance of your

vacation travels and at a lower rate compared to the market rate.

• You are planning to travel and are forewarned of a possible

cyclone in that place. Based on that warning, you postpone your

visit. Later, you find that the cyclone created havoc, and you

avoided a terrible situation.

We can imagine many similar scenarios that are made possible by analyzing data

about you and your activities that is collected through various means—including your

Google searches, visits to various web sites, your comments on social media sites, your

activities using various computer applications, and more. The use of data analytics in

these scenarios has focused on your individual perspective.

Now, let’s look at scenarios from a business perspective. Imagine these situations:

• You are in the hotel business and are able to provide competitive

yet profitable rates to your prospective customers. At the same

time, you can ensure that your hotel is completely occupied all

the time by providing additional benefits, including discounts

on local travel and local sightseeing offers tied into other local

vendors.

• You are in the taxi business and are able to repeatedly attract

the same customers based on their earlier travel history and

preferences of taxi type and driver.

• You are in the fast-food business and offer discounted rates to

attract customers on slow days. These discounts enable you to

ensure full occupancy on those days also.

• You are in the human resources (HR) department of an

organization and are bogged down by high attrition. But now you

are able to understand the types of people you should focus on

recruiting, based on the characteristics of those who perform well

and who are more loyal and committed to the organization.

• You are in the airline business, and based on data collected by

the engine system, you are warned of a potential engine failure in

the next three months. You proactively take steps to carry out the

necessary corrective actions.

• You are in the business of designing, manufacturing, and selling

medical equipment used by hospitals. You are able to understand

the possibility of equipment failure well before the equipment

actually fails, by carrying out analysis of the errors or warnings

captured in the equipment logs.

Chapter 1 ■ Overview Of Business analytiCs

All these scenarios are possible by analyzing data that the businesses and others

collect from various sources. There are many such possible scenarios. The application of

data analytics to the field of business is called business analytics.

You have likely observed the following scenarios:

• You’ve been searching for the past few days on Google for

adventurous places to visit. You’ve also tried to find various travel

packages that might be available. You suddenly find that various

other web sites you visit or the searches you make show a specific

advertisement of what you are looking for, and that too at a

discounted rate.

• You’ve been searching for a specific item to purchase on Amazon

(or any other site). Suddenly, on other sites you visit, you find

advertisements related to what you are looking for or find

customized mail landing in your mailbox, offering discounts

along with other items you might be interested in.

• You’ve also seen recommendations that Amazon makes based

on your earlier searches for items, your wish list, or previous

Amazon purchases. Many times you’ve also likely observed

Amazon offering you discounts or promoting products based on

its available data.

All of these possibilities are now a reality because of data analytics specifically used

by businesses. This book takes you through the exciting field of business analytics and

enables you to step into this field as well.

1.1 Objectives of This Book

Many professionals are becoming interested in learning analytics. But not all of them

have rich statistical or mathematical backgrounds. This book is the right place for

techies as well as those who are not so techie to get started with business analytics.

You’ll start with a hands-on introduction to R for beginners. You’ll also learn about

predictive modeling and big data, which forms a key part of business analytics. This is an

introductory book in the field of business analytics using R.

The following are some of the advantages of this book:

• This book covers both R programming and analytics using

numerous real-life examples.

• It offers the right mix of theory and hands-on labs. The concepts

are explained using business scenarios or case studies where

required.

• It is written by industry professionals who are currently working

in the field of analytics on real-life problems for paying customers.

Chapter 1 ■ Overview Of Business analytiCs

This book provides the following:

• Practical insights into the use of data that has been collected,

collated, purchased, or available for free from government

sources or others. These insights are attained via computer

programming, statistical and mathematical knowledge, and

expertise in relevant fields that enable you to understand the data

and arrive at predictive capabilities.

• Information on the effective use of various techniques related to

business analytics.

• Explanations of how to effectively use the programming platform

R for business analytics.

• Practical cases and examples that enable you to apply what you

learn from this book.

• The dos and don’ts of business analytics.

• The book does not do the following:

• Deliberate on the definitions of various terms related to

analytics, which can be confusing

• Elaborate on the fundamentals behind any statistical or

mathematical technique or particular algorithm beyond

certain limits

• Provide a repository of all the techniques or algorithms used

in the field of business analytics (but does explore many of

them)

1.2 Confusing Terminology

Many terms are used in discussions of this topic— for example, data analytics, business

analytics, big data analytics, and data science. Most of these are, in a sense, the same.

However, the purpose of the analytics, the extent of the data that’s available for analysis,

and the difficulty of the data analysis may vary from one to the other. Finally, regardless

of the differences in terminology, we need to know how to use the data effectively for our

businesses. These differences in terminology should not come in the way of applying

techniques to the data (especially in analyzing it and using it for various purposes

including understanding it, deriving models from it, and then using these models for

predictive purposes).

In layman’s terms, let’s look at some of this terminology:

• Data analytics is the analysis of data, whether huge or small, in

order to understand it and see how to use the knowledge hidden

within it. An example is the analysis of the data related to various

classes of travelers (as noted previously).

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