Data Science at the Command Line

Chapter 7. Exploring Data

Now that we have obtained and scrubbed our data, we can continue with the third step of the OSEMN model, which is to explore it. After all that hard work (unless you already had clean data lying around!), it’s time for some fun.

Exploring is the step where you familiarize yourself with the data. Being familiar with the data is essential when you want to extract any value from it. For example, knowing what kind of features the data has, means you know which ones are worth further exploring and which ones you can use to answer any questions that you have.

Exploring your data can be done from three perspectives. The first perspective is to inspect the data and its properties. Here, we want to know, for example, what the raw data looks like, how many data points the data set has, and what kind of features the data set has.

The second perspective from which we can explore out data is to compute descriptive statistics. This perspective is useful for learning more about the individual features. One advantage of this perspective is that the output is often brief and textual and can therefore be printed on the command line.

The third perspective is to create visualizations of the data. From this perspective, we can gain insight into how multiple features interact. We’ll discuss a way of creating visualizations that can be printed on the command line. However, most visualizations are best displayed on graphical user interfaces. An advantage of visualizations over ...