I started to write some specific suggestions but since you were going to be doing some more writing before the next meeting I thought I'd hold off. Here are some general thoughts though, as well as a stab that might help things along.
I. Intro: should be Sagany-wonderous and inviting, where possible humorous. An attempt at contribution: (I just picked up Rethinking v1 and I feel like we're pretty much doing a similar preface here though hopefully less technical)
Data is created by all things. It surrounds us and penetrates us; it binds the galaxy together. ~ Obi-Wan Kenobi (paraphrased)
Regardless of background, and whether we're conscious of it or not, we are constantly inundated with data. It's inescapable, from our first attempts to understand the world around us, to our most recent efforts to figure out why we still don't understand it. But if you're here reading this, you are the type of person that wants to keep trying! So for seasoned professionals or perhaps just the data curious, we want to help you learn more about how to use data to answer the questions you have.
If you consider yourself a data scientist, an analyst, or a statistical hobbyist, you already know that the best part of a good dive into data is the modeling. No matter what part of the data world you find yourself living, models give us the possibility to answer questions, make recommendations, and understand what we're interested in a little bit better. But no matter who you are, it isn't always easy to understand how the models work. And even when you do get a good grasp, it can get complicated and there are a lot of details, or maybe you just have other things going on in your life and have forgotten a few things , and it's always good to remind yourself of the basics.
Your humble authors have struggled mightily themselves throughout the course of their data history, and still do! We were initially taught by those that weren't exactly experts, and often found it difficult to get a good grasp of statistical modeling and machine learning. We've had to learn how to use the tools, how to interpret the results, and possibly the most difficult, how to explain what we're doing to others! We've forgotten a lot, confused ourselves, and just made some nifty blunders in the process. That's okay! Our goal here is to help you avoid some of those pitfalls, and to help you understand the basics of how models work, and get a sense of how most modeling endeavors have a lot of things in common.
While we might be understanding the world a little better, do we always understand what our models are doing at a fundamental level? Our goal is to show you the basics of how models work -- using both pre-existing tools and by getting your hands dirty -- and how to interpret the results from those models.
add section to chapters: Uncertain Excursions to briefly discuss the
While we could get philosophical and very complicated, here's a perspective that we generally have, that really won't matter but provides a little bit of context of where we're coming from. We're may use the terms like statistics, machine learning, deep learning, and artificial intelligence, but we're not going to be too concerned about the differences between them. though here's a general idea of what we mean when we use them:
stats - a modeling approach focused on general uncertainty ML - a modeling approach focused on prediction DL - ML with neural networks AI - DL with a focus on mimicking human intelligence
II. Structure/Expectation. I wonder if we can even make this bullet-pointy