Chapter 3. Turning Data into Information
Raw data can be in many different formats and of varying quantity and quality. Sometimes, we are overwhelmed with data, and sometimes we struggle to get every last drop of information from our data. For data to become information, it requires some meaningful structure. We often have to deal with incompatible formats, inconsistencies, errors, and missing data. It is important to be able to access different parts of the dataset or extract subsets of the data based on some relational criteria. We need to spot patterns in our data and get a feel for how the data is distributed. We can use many tools to find this information hidden in data from visualizations, running algorithms, or just looking at the data in a spreadsheet.
In this chapter, we are going to introduce the following broad topics:
- Big data
- Data properties
- Data sources
- Data processing and analysis
But first, let's take a look into the following explanations:
