Welcome to module 3. In the last module, as Ron explained through a funny example of cloudy with a chance of meatballs. When it comes to data science, quantity is not the only concern. It's the quality also that we need to pay close attention to. Well, this may sound almost like a cliche to talk about quality. However, it's not easy to have quality data. With data analysis, it's garbage in, garbage out. The quality of data dictates the quality of analysis which in turn determines the quality of decisions that you make. Therefore, in this module, we are going to focus on the following things. First, we will quickly discuss what's data quality and why is it important and why should a firm consider investing in generating quality data? Yes, generating quality data is a deliberate business investment decision. Then after having this high level view of data related issues, we'll discuss data preparation. The first thing in data preparation is to be planning for it. Here we'll discuss how your business problem dictates your data preparation. Later in the module after having discussed data preparation, we will see how do we prepare data using some of the interesting packages in R.