Preface

I wrote this book for the person who has just been handed a spreadsheet and a question. Maybe you work in a clinic, a hospital, a health plan, or a public health office, and analytics was never part of your training. You are comfortable enough with a computer, but “data analytics” sounds like a walled garden that other people were let into. It is not. The core of this work is careful thinking, and you already do that every day. What you need is a disciplined way to turn healthcare records into trustworthy answers, and that is what these fourteen chapters teach.

My first commitment is that every idea arrives through a healthcare example, not an abstract one. You will meet a clinic worried about missed appointments, an emergency department watching its length of stay, a finance team trying to understand cost, and a care team deciding whom to call first. The same no-show problem runs quietly through the whole book, growing from a vague worry in Chapter 1 into a fair, measured decision in Chapter 14. I chose that continuity on purpose. Real analysis is a chain, and I wanted you to watch one build link by link.

My second commitment is that the tools stay honest about what they are. We use Microsoft Excel for most of the book because it is accessible, widely available, and visible: you can see the rows, the formulas, and the charts in one place. Later chapters add a small amount of Python, not to replace Excel but to show where you go when a spreadsheet reaches its limits. I am not trying to make you a programmer. I am trying to make you an analyst who knows which tool fits the question.

You learn this material by doing it. Every chapter works through a real example on the teaching datasets that come with the book, and every chapter ends with a short exercise and a set of problems. Do them. Open the files, compute the numbers, and check your answers against the ones I show. The datasets are synthetic, which means you can practice freely without touching real patient information, and that safety is deliberate. When you move to real data, the habits you built here, checking a key, naming what a row represents, questioning a blank, will still hold.

A last word, because it matters more than any formula. Healthcare data describes people, and the same number that could target help could also target harm. I have tried to keep that responsibility in view throughout, and I ask you to carry it too. The goal of this book is not only that you can produce a chart or fit a model. It is that you can turn healthcare data into useful action while respecting context, quality, privacy, and uncertainty. If you finish these chapters thinking that way, the book has done its job.