Summary

In this chapter, we learned how to bring data into DataRobot. We learned how to assess data quality and to perform EDA by using DataRobot's features. We saw how DataRobot makes it very easy to explore data, set up target features, and perform correlation (or, more accurately, association analysis).

We learned how to leverage DataRobot's output to gain a better understanding of our problem and dataset, and then how to create feature lists to be used in model building. You could do these tasks in Python or R and they are not very difficult, but they do consume some time. This time is better served in focusing on understanding the problem and the dataset.

In the next chapter, we will jump into something that most of you must be waiting for: building models.