Now that we have a basic introduction to the world of data science and understand why the field is so important, let's take a look at the various ways in which data can be formed. Specifically, in this chapter, we will look at the following topics:
- Structured versus unstructured data
- Quantitative versus qualitative data
- The four levels of data
We will dive further into each of these topics by showing examples of how data scientists look at and work with data. The aim of this chapter is to familiarize ourselves with the fundamental ideas underpinning data science.
In the field, it is important to understand the different flavors of data for several reasons. Not only will the type of data dictate the methods used to analyze and extract results, but knowing whether the data is unstructured, or perhaps quantitative, can also tell you a lot about the real-world phenomenon being measured.
The first thing to note is my use of the word data. In the last chapter, I defined data as merely being a collection of information. This vague definition exists because we may separate data into different categories and need our definition to be loose.
The next thing to remember while we go through this chapter is that for the most part, when I talk about the type of data I will refer to either a specific characteristic of a dataset or to the entire dataset as a whole. I will be very clear about which one I refer to at any given time.