Raw data consists of long lists of numbers and labels that don't seem to be very informative. Raw data lacks context. Exploratory data analysis is what you use to make sense of the data. You do this by converting data from this raw form to a form that makes sense, that has context, that tells the story you want to tell. Basically, exploratory data analysis consists of organizing and summarizing raw data, looking for important features and patterns in the data, looking for any striking deviations from those patterns, and interpreting your findings in the context of the problem or research question. We'll begin exploratory data analysis by looking at one variable at a time, also called univariate analysis. In order to convert raw data into useful information, we need to summarize and then examine the distribution of any variables of interest. By distribution of a variable, we mean what values the variable takes, and how often the variables takes those values. >> If we were studying a small number of observations, we could do this with a pencil and paper, a calculator, or even in our heads. The data sets you're working with often have thousands of observations. Working with such large samples is only achievable if we use statistical software. These software programs require the use of Syntax or Formal Code to retrieve, analyze and manipulate data. Learning to write code, learning the proper use of syntax, can really expand your capacity for engaging in statistical applications. And is an essential skill you will learn in this course. This skill will also greatly expand your capacity for engaging in deeper levels of quantitative reasoning about data. >> For this course you'll be using SAS studio. SAS is a very powerful statistical software package. Looking at all the windows, menus, and features though can be rather daunting. So it's important for you to realize that this course will introduce you to the basics of SAS. You will learn what you need to know to get started asking and answering different questions about data. >> In the beginning it may feel like you're learning another language, basically you are. As you work through your project you should begin to feel more comfortable implementing the various decisions you'll be making about the data. When you need help, seek it from your course moderator, professor, your peers, or from course discussions. >> But do I write R? >> No, no, no. >> Now you need to register for SAS OnDemand for Academics. >> To register for SAS, follow the instructions in the available document called getting started with SAS.