Summary

In this introductory chapter, we made extensive use of pandas to load and explore the case study data. We learned how to check for basic consistency and correctness by using a combination of statistical summaries and visualizations. We answered such questions as "Are the unique account IDs truly unique?", "Is there any missing data that has been given a fill value?", and "Do the values of the features make sense given their definition?"

You may notice that we spent nearly all of this chapter identifying and correcting issues with our dataset. This is often the most time-consuming stage of a data science project. While it is not necessarily the most exciting part of the job, it gives you the raw materials necessary to build exciting models and insights. These will be the subjects of most of the rest of this book.

Mastery of software tools and mathematical concepts is what allows you to execute data science projects, at a technical level. However...