The second section of this book will cover a variety of topics that are liable not to show up in a given data science project. This doesn't mean that knowing them is optional for a professional data scientist, but it does mean that you might not be put on the spot about them until somewhat later in your career. My goal is to fill those holes ahead of time.
The topics here cover a wide range. There are very general-purpose analytics tools that almost made it into the first part, such as clustering. Most software engineering concepts, beyond basic scripting, fit into this chapter was well. Finally, there are very specialized areas such as natural language processing, which some data scientists never use at all.