Request PDF on ResearchGate | R for Business Analytics | R for Business Analytics looks at some of the most common tasks performed by business analysts. R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth Download book PDF . Introduction. The plethora of textbooks on business analytics and similar topics has received yet another member, this time by authors Umesh.
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Business Intelligence (BI). It refers to skills, technologies, applications and practices used to help a business acquire a better understanding of. Data Mining and Business Analytics with R, by Johannes Ledolter; Publisher: Wiley (), A pdf file of the book can be downloaded by following the link. R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and .
With increasing desktop computing power and companies amassing massive amounts of data, business decisions are becoming more and more data-based. This holds in many sectors, but in particular in banking, insurance, investments, retailing, electronic commerce, advertising, and direct marketing.
Because of this new approach to business, companies are in need of people with a new set of computational skills. There is also an increasing notion that in order to stay competitive, managers need to be re-equipped with long-lost analytical skills. In fact, there is often a disconnect between the people who run analytics such as statisticians, data miners, and computer scientists and management who may have a background in marketing or finance but not very much technical training.
One goal of this book is to provide management with a better appreciation of the value of data analytics. This book is purposefully clean of mathematics and formulas.
This is not to say that mathematics is unimportant — on the contrary, mathematics plays an important role in the development of statistical models and methods. However, the focus in this book is not on the development of statistical methods but rather on the application of statistical thinking to business problems. Based on our own teaching experience, too much mathematical detail often confuses and sometimes even scares the inexperienced and novice user of statistical methods.
Therefore, the goal of this book is to explain statistical concepts mainly in plain English, abstaining from the use of mathematical symbols and equations as much as possible. We are aware that this approach can sometimes lead to statements and explanations that are slightly imprecise at least in a mathematical sense , but our overarching goal is to train business leaders and managers to appreciate statistics and to adopt the findings of data-driven decision making into their own language.
Thus a treatment of analytics in plain English is essential. Most data mining books focus on the trained expert either from computer science, statistics, or mathematics and as such emphasize algorithms and methods over intuition and business insight.
Most data mining books also cover a wide range of data mining algorithms, such as neural networks, trees, or support vector machines. The focus in this book is not so much on the many different algorithms that are available many of them tackling similar problems, such as classification or prediction but rather on the differences in data and business scenarios that require different types of analytical approaches and ideas.
As such, this book will not provide the same breadth of coverage of different algorithms as traditional data mining books. Instead, it will focus on a few select algorithms and models and explain the differences they make for business decision making. Well, probably the best answer is that we envision this book to be a valuable resource for business students and managers who do not have much of a background in statistics or mathematics but who wish to get a better 6 1 Introduction appreciation of data and data-driven decision making.
This book focuses a lot on intuition and insight. It discusses many different data scenarios and related business questions that might arise.
Then, it illustrates different ways of extracting new business knowledge from this data. This book is not exhaustive in that it does not cover everything that there is to know when it comes to data mining for business. We believe that knowing every single detail cannot be the goal for a manager. Rather, our goal is to communicate concepts of statistics and data mining in nonthreatening language, to create excitement for the topic, and to illustrate in a hands-on and very concrete fashion how data can add value to the everyday life of business executives.
In Chapter 2, we introduce data exploration. By data exploration we mean both numerical and graphical ways of understanding the data. Data exploration is probably the single most important step of any data analysis — yet, it is also the least appreciated and most neglected one.
The reason is that with the availability of powerful algorithms embedded into userfriendly software, most users will jump directly into building complex models and methods without ever getting a clear understanding of their data. We will spend quite some time discussing a wide array of data explorations in Chapter 2.
The reason is that data can be very complex — in fact, chances are that our data is more complex and complicated than we initially believed. Unleashing powerful algorithms and methods on such data can have detrimental results, ranging from inaccurate predictions to complete meaninglessness of our results. Hence, we advocate that data needs to be explored first in a very careful manner.
Only when we can be sure that we understand every single detail of our data patterns, trends, unusual observations, and outliers can we apply models and methods with peace of mind.
Subsequent chapters cover different aspects of data modeling. We start in Chapter 3 by introducing basic modeling ideas. We discuss model interpretation and evaluation and distinguish statistical significance of the results from practical relevance. In Chapter 4, we introduce a few key ideas to make models more flexible. Clearly, this may not be appropriate in all business scenarios: we may be willing to believe 1. If so, then we should worry about making our model flexible enough — and the precise details are covered in Chapter 4.
In Chapter 5, we cover yet another important aspect of model building: making models selective. For instance, while we may think that using aggregate household characteristics in addition to marketing expenditures will result in a better forecasting model for sales, this is not an automatic conclusion.
What if our marketing expenditures are allocated as a function of household characteristics? For instance, what if we had decided to allocate more marketing resources in zip codes with higher median household incomes? Would household income then still add value for modeling sales? Would it contain much additional information above and beyond the information that already resides in marketing expenditures? What we will focus on in this chapter are a few established approaches that will help overcome some of the lingering shortcomings of the previous chapters.
A possible course sequence could be as follows: Lecture 1 Data exploration, visualization, and discovery Chapter 2 Lecture 2 Basic modeling concepts, least squares regression, and interpretation Sections 3. Data exploration is probably the single most important step in any data analysis.
So, why do we perform data exploration?
The answer is very simple: to better understand our data and get intimately familiar with it. We simply should not base business decisions on complex methods and models unless we are certain that these methods capture the essence of our data. For instance, much of this book will talk about linear models.
But what if the reality is not quite linear? I like this book because of the interesting topics this book covers including text mining, social network analysis and time series modeling. Having said this, the author could have put in some effort on the formatting of this book which is pure ugly. At times you will feel you are reading a masters level project report while skimming through the book. However, once you get over this aspect the content is really good to learn R.
However trust me, apart from a few minor issues Rattle is not at all bad. I really hope they keep working on Rattle to make it better, as it has a lot of potential.
It is much better than the base graphics that comes pre-installed with R, so I would recommend you start directly with ggplot 2 without wasting your time on base graphics. However, if you want to get to further depths of ggplot-2 then this is the book for you. Though I prefer ggplot 2, Lattice is another package at par with ggplot 2.
The author of this book has extensive experience in R coding and that is evident when you read this book. I must warn you that at times while reading this book one wonders about the utility of some of the things Mr. Matloff talks about. Nevertheless, this is the best book in the market to learn R programming. The author also touches on the issues of parallel computing in R — a topic highly relevant in the day and age of big data.
Before jumping to the books, I recommend you take this free online course. Front Matter Pages i-xviii. Why R. Pages R Infrastructure. R Interfaces. Manipulating Data.
Building Regression Models. Data Mining Using R. Clustering and Data Segmentation. Forecasting and Time Series Models.