Fix intro links to section intros (#259)

Author: akgold

chapters/intro.qmd

@@ -28,8 +28,8 @@ the background, and there's no \index{Google Sheets}Google Sheet,
 \index{csv}CSV file, or half-baked database query in sight.
 
 But that's a myth. If you're a data scientist putting your work in front
-of someone else's eyes, you are in production. And, I believe, if
-you're in production, this book is for you.
+of someone else's eyes, you are in production. And, I believe, if you're
+in production, this book is for you.
 
 You may sensibly ask who I am to make such a proclamation.
 
@@ -45,8 +45,8 @@ science products more robust with open-source tooling and
 \index{Posit}Posit's Professional Products.
 
 I've seen organizations at every level of data science maturity. For
-some organizations, in production means a report that gets rendered
-and emailed around. For others, it means hosting a live app or dashboard
+some organizations, in production means a report that gets rendered and
+emailed around. For others, it means hosting a live app or dashboard
 that people visit. For the most sophisticated, it means serving live
 predictions to another service from a machine learning model via an
 application programming interface (\index{API}API).
@@ -230,14 +230,14 @@ skills to get them into production when it's time.
 
 To that end, this book is divided into three parts.
 
-[Part 1](@sec-1-intro) is about applying DevOps best practices to a data
+[Part 1](sec1/1-0-sec-intro.qmd#sec-1-intro) is about applying DevOps best practices to a data
 science context. Adhering to these best practices will make it easier to
 take projects into production and ensure their security and stability
 once they're there. While these best practices are inspired by DevOps,
 data science and data science projects are different enough from
 general-purpose sofware engineering that some re-thinking is required.
 
-[Part 2](@sec-2-intro) is a walkthrough of basic concepts in IT
+[Part 2](sec2/2-0-sec-intro.qmd#@sec-2-intro) is a walkthrough of basic concepts in IT
 Administration that will get you to the point of being able to host and
 manage a basic data science environment. If you are a hobbyist or have
 only a small data science team, this might make you able to operate
@@ -246,7 +246,7 @@ significant IT/Admin support, it will equip you with the vocabulary to
 talk to the IT/Admins at your organization and some basic skills of how
 to do IT/Admin tasks yourself.
 
-[Part 3](@sec-3-intro) is about how everything you learned in Part 2 is
+[Part 3](sec3/3-0-sec-intro.qmd#@sec-3-intro) is about how everything you learned in Part 2 is
 inadequate at organizations that operate at enterprise scale. If Part 2
 explains how to do IT/Admin tasks yourself, Part 3 is my attempt to
 explain why you shouldn't.
@@ -298,10 +298,10 @@ length and mass, and we're going to build up an entire data science
 environment dedicated to exploring that relationship.
 
 The front end of this environment will be a website built with the
-\index{Quarto}Quarto publishing system. It will include an app for fetching penguin mass
-predictions from a machine learning model based on bill length and other
-features. The website will also have pages dedicated to exploratory data
-analysis and model building.
+\index{Quarto}Quarto publishing system. It will include an app for
+fetching penguin mass predictions from a machine learning model based on
+bill length and other features. The website will also have pages
+dedicated to exploratory data analysis and model building.
 
 On the backend, we will build a data science workbench on an AWS
 \index{EC2}EC2 instance where we can do this work. It will include