This book is basically a first course in analytics using
SAS. Readers will get the basic foundational knowledge needed to be successful
in the field. It has 13 chapters, each uniquely designed to develop a
step-by-step understanding of the subject. Numerous real life examples are
given with data and SAS code so that the readers, after completing this book,
feel the comfortable on their assignments with actual business challenges.
Here’s a rundown of the content in each chapter:
- Chapter 1 starts with the basics of business analytics and some use cases to build a background for the upcoming chapters. We cover some of the most widely used analytical techniques. Next we move on to analytical tools including the most widely used analytics tool in technology today: SAS.
- Chapters 2, 3, and 4 impart the basic code-level knowledge in SAS software. In these chapters, we discuss the basics of SAS, followed by data handling in SAS, and, finally, important SAS functions and procedures. These chapters build the basic foundation in SAS software, which will be required throughout this book, while working with real life business scenarios on business analytics.
- Chapters 5 and 6 offer the basics of statistics and simple descriptive statistical techniques. They are the foundation-building chapters in statistics.
- Chapter 7 proceeds—since you have a solid foundation on the basics of statistics and SAS—to the lifecycle steps in analytics. Any analytical problem solving starts with data exploration, validation, and data cleaning. In this chapter, we document some of the most creative data-cleaning techniques used in the industry.
- Chapter-8 is all about testing. Testing of hypotheses is a concept that is linked to many other statistical topics. In this chapter, we have explained the testing of hypothesis theory in very simple terms.
- Chapter 9 begins the journey into advanced topics. The first step in predictive modeling is correlation and regression. We explain these concepts with abundant, real life examples.
- Chapter 10 shows that regression is an ocean. We discuss multiple regressions in this chapter, as well as concepts like multicolliniarity and adjusted R-square.
- Chapter 11 explains why logistic regression is a commonly used predictive modeling technique. In this chapter, we discuss model building using logistic regression.
- Chapter 12 demonstrates time series analysis, along with its applications, as well as how to implement the ARIMA technique presented.
- Chapter 13 is all about big data. Considering the exponential growth and popularity of the big data analytics domain, we felt that it would be appropriate to end this book with basics of big data. This final chapter gives the basics of big data with real-time data and worked-out examples, which demonstrates the code and use of Hadoop.
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