Clean up part 2 introduction

book/src/pages/part-two-introduction.md

@@ -5,19 +5,28 @@ In the second part of this book we are aiming to
 1. deepen our understanding of structural recursion, by using it in new contexts;
 2. learn how to manipulate collections of data using standard methods `map`, `filter`, `flatMap`, and `foldLeft`;
 3. define and use custom data structures of a particular kind known as algebraic data types; and
-3. learn a model for understanding data known as boxes and arrows diagrams.
+4. learn a model for understanding data layout in memory, known as boxes and arrows diagrams.
 
-We'll encounter these topics in the context of *exploratory data analysis*, 
-which, as the name suggests, is the process of exploring data in the hopes of finding interesting things within it.
-Concretely, this means we'll be transforming data, visualizing it, and calculating statistics of it.
-Exploratory data analysis is part of the larger field of *data science*, which lies at the intersection of computer science and statistics.
-We will take a computational approach to statistics.
-This means we'll get the computer, rather than fancy mathematics, to do the work for us,
-which I think leads to a clearer understanding of these topics (particularly if you are a programmer!)
+We'll encounter these topics in the context of [data science][data-science]. 
+Data science is the field concerned with finding interesting things in data.
+This brings together elements of computing, statistics, and graphic design (in the form of visualization.)
+
+Data science allows us to address questions about the world such as **TODO: insert examples**
+In this part we'll use a variety of data sets to explore these questions.
+If you have particular data you're interested in exploring, I encourage you to do so in this part.
+There is no better way to learn than to work on questions that are meaningful to you.
+
+This part assumes little experience with statistics.
+When we meet statistcal concepts,
+we'll approach them from a computational, rather than mathematical, perspective.
+This means we'll get the computer to do the work for us,
+and we'll avoid a lot fancy mathematics that is typically found in statistics courses.
+I think this leads to a clearer understanding of these topics (particularly if you are a programmer!)
 
 
 ## Setup
 
 To work through this part of the book, use the [creative-scala-data-template][template] from Github.
 
+[data-science]: https://en.wikipedia.org/wiki/Data_science
 [template]: https://github.com/creativescala/creative-scala-data-template