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Practical Business Analytics Using SAS: A Hands-on Guide shows SAS users and businesspeople how to analyze data effectively in real-life business scenarios.

The book begins with an introduction to analytics, analytical tools, and SAS programming. The authors—both SAS, statistics, analytics, and big data experts—first show how SAS is used in business, and then how to get started programming in SAS by importing data and learning how to manipulate it. Besides illustrating SAS basic functions, you will see how each function can be used to get the information you need to improve business performance. Each chapter offers hands-on exercises drawn from real business situations.

The book then provides an overview of statistics, as well as instruction on exploring data, preparing it for analysis, and testing hypotheses. You will learn how to use SAS to perform analytics and model using both basic and advanced techniques like multiple regression, logistic regression, and time series analysis, among other topics. The book concludes with a chapter on analyzing big data. Illustrations from banking and other industries make the principles and methods come to life.

Readers will find just enough theory to understand the practical examples and case studies, which cover all industries. Written for a corporate IT and programming audience that wants to upgrade skills or enter the analytics field, this book includes:

More than 200 examples and exercises, including code and datasets for practice. Relevant examples for all industries.Case studies that show how to use SAS analytics to identify opportunities, solve complicated problems, and chart a course.

Practical Business Analytics Using SAS: A Hands-on Guide gives you the tools you need to gain insight into the data at your fingertips, predict business conditions for better planning, and make excellent decisions. Whether you are in retail, finance, healthcare, manufacturing, government, or any other industry, this book will help your organization increase revenue, drive down costs, improve marketing, and satisfy customers better than ever before.

Inhaltsverzeichnis

Frontmatter

Basics of SAS Programming for Analytics

Frontmatter

Chapter 1. Introduction to Business Analytics and Data Analysis Tools

Abstract
There is an ever-increasing need for advanced information and decision support systems in today’s fierce global competitive environment. The profitability and the overall business can be managed better with access to predictive tools—to predict, even approximately, the market prices of raw materials used in production, for instance. Business analytics involves, among others, quantitative techniques, statistics, information technology (IT), data and analysis tools, and econometrics models. It can positively push business performance beyond executive experience or plain intuition.
Venkat Reddy Konasani, Shailendra Kadre

Chapter 2. SAS Introduction

Abstract
Chapter 1 introduced SAS, the most widely used tool in the world of analytics. SAS is a software suite that can retrieve data from a variety of data sources. It can help you clean the data and perform statistical operations on it. For nontechnical users, it also provides a graphical user interface (GUI) to perform various analytics operations. The soul of SAS is its programming language, which is used by most analysts. It provides more advanced data handling and analytical capabilities than the GUI. The SAS programming language, also known as the SAS scripting language, is much easier to learn than most other programming languages such as FORTRAN, C, and Java.
Venkat Reddy Konasani, Shailendra Kadre

Chapter 3. Data Handling Using SAS

Abstract
After learning the basics of the SAS tool in the previous chapter, you can now learn about data handling in SAS, the main focus of this chapter. While learning any analytics tool, you should be aware of three main phases: tool basics, basic data handling, and important functions and statistical algorithms (Figure 3-1). Any analyst who wants to work with advanced statistical techniques needs to have a fair understanding of these three areas of a statistical or analytical tool like SAS. There are also more advanced topics such as using macros and other tricks to write efficient code, but you can learn about those topics as your familiarity with the tool increases.
Venkat Reddy Konasani, Shailendra Kadre

Chapter 4. Important SAS Functions and Procs

Abstract
In the previous chapter, you learned about some important data manipulation techniques. You will typically take the raw data and prepare it for analysis. Once the final data is ready, you can go ahead with the analysis. The analysis might involve representing simple aggregated tables in meaningful graphs, applying simple descriptive statistics to advanced analytics, and predictive modeling. You may need advanced algorithms and functions to perform the analysis. While learning an analytics tool, it’s vital to know how to use some of the important functions and algorithms.
Venkat Reddy Konasani, Shailendra Kadre

Using SAS for Business Analytics

Frontmatter

Chapter 5. Introduction to Statistical Analysis

Abstract
This and subsequent chapters will delve into the details of business analytics techniques. It has already been established in the previous chapter that statistics forms a major portion of this art. This chapter will begin with the basic definition of statistics. It will also refer to a few web sites to access data sets, which you can use for the examples. By the end of this chapter, you will be able to comprehend the following concepts that are essential for proceeding with business analytics techniques:
Venkat Reddy Konasani, Shailendra Kadre

Chapter 6. Basic Descriptive Statistics and Reporting in SAS

Abstract
The first step in statistical data analysis is to define the business objectives, which determines the need of the project. This step will require some initial planning. Once the data is gathered in the required format, the next step is to explore the data.
Venkat Reddy Konasani, Shailendra Kadre

Chapter 7. Data Exploration, Validation, and Data Sanitization

Abstract
Preparing the data for the actual analysis is an important portion of any analytics project. The raw data comes from a variety of sources such as classical relational databases, flat files, spreadsheets, and unstructured data from sources such as social media text. A project may contain both structured and unstructured data, and to add to the complexity, there can be numerous data sources. As you would expect, the data will have a lot of challenges—both in quality and in quantity. An analyst needs to first read the data from its sources, which itself can be a challenging task, and then parse it to be useful for any further analysis. SAS needs data to be in its own datasets before you can use any of its routines for analysis. In short, the raw data is not always ready for the analysis; it needs to be validated and cleaned before the analysis.
Venkat Reddy Konasani, Shailendra Kadre

Chapter 8. Testing of Hypothesis

Abstract
You should now have an understanding of the basics of SAS and the fundamentals of statistics. You’ve mostly used descriptive statistics to explain the data and get some quick insights without applying any advanced techniques. One advanced technique you’ll learn to apply in this chapter is how to test your hypotheses. Learning how to test a hypothesis is important for analysts because they will use the process in many situations, such as when testing correlation, testing regression coefficients, testing parameter estimates in time-series analysis, testing the goodness of fit in logistic regression, and so on. You’ll learn about those topics in the coming chapters.
Venkat Reddy Konasani, Shailendra Kadre

Chapter 9. Correlation and Linear Regression

Abstract
In the previous chapter, we covered how to prepare data for model building. In this chapter, we discuss model building, which may be the most important step in a data analytics project. We will discuss the most popular technique of model building—linear regression. We will first discuss correlation in detail and then discuss the differences between correlation and regression. Finally, we will show a detailed example of modeling using linear regression. We will explain the concepts using real-world scenarios wherever possible.
Venkat Reddy Konasani, Shailendra Kadre

Chapter 10. Multiple Regression Analysis

Abstract
In Chapter 9, we discussed correlation, which is used for quantifying the relation between a pair of variables. We also discussed simple regression, which helps predict the dependent variable when an independent variable is given. In simple regression, you use a single independent variable to predict the dependent variable. This is a simplistic approach, and in practice some dependent variables may require more than one independent variable for accurate predictions. For example, can you predict the gross domestic product (GDP) of a nation by looking just at exports? The obvious answer is that it can’t be done. Predicting the GDP may need several other variables, such as per-capita income, value of natural resources, national debt, and so on. Likewise, the health of an individual depends upon many variables, such as smoking or drinking habits, eating habits, job pressure, daily workouts, genetics, sleeping habits, and more.
Venkat Reddy Konasani, Shailendra Kadre

Chapter 11. Logistic Regression

Abstract
In previous chapters, we covered correlation and linear regression modeling in detail. If you look to quantify the relationship between two variables, you use the correlation coefficient. For example, you can quantify the relation between salary and expenses using correlation. If you needed to predict a response variable based upon some other item, you could use linear regression modeling, provided the relationship is linear. For example, if you want to predict exactly what a person’s expenses will be when his salary is $10,000, you can use linear regression modeling, provided the expense and salary fit on a straight-line graph. In some cases, this relationship is not actually linear, but you can make it linear by applying some simple mathematical transformations; still, you can use linear regression modeling.
Venkat Reddy Konasani, Shailendra Kadre

Chapter 12. Time-Series Analysis and Forecasting

Abstract
In this chapter, we introduce the concept of time-series forecasting straight using a couple of examples. We’ll talk about a popular bank, which has been offering credit cards to its customers for more than a decade. Every month the bank has some customers who file for bankruptcy. Some customers have defaulted on their loans for more than six months, and finally they need to be taken off the books. Obviously, this is a loss to the bank, in terms of dollars and accounts. Can the bank proactively estimate these losses for each of its portfolios? Can the bank proactively take necessary steps to control the loss and find out whether it is going to soar in the future? How does the bank forecast such losses based on historical data?
Venkat Reddy Konasani, Shailendra Kadre

Chapter 13. Introducing Big Data Analytics

Abstract
By this time you have read 12 chapters on the topic of business analytics and can now appreciate the theories and practices that make up this domain. But the learning never ends, especially in a fast-growing area like this one. By now, you might already be aware that big data is catching up in popularity very quickly. A fast-growing number of organizations, notably online ones with a large amount of visitor traffic, are generating an unprecedented amount of data daily, which is almost impossible to handle with classic techniques.
Venkat Reddy Konasani, Shailendra Kadre

Backmatter

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