Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning 1st Edition

IMHO, this is a really good overview of the data science industry today (circa 2021). Two things that stood out to me: 1) the emphasis on practical explanations (this is what it really means) and 2) the emphasis on pre-technical analysis components of data science (what I am referring to as the MORE framework representing consideration of missing data, origin story of data, representativeness of the data to what you are investigating, and exploration through visualization). For a day read, this was an awesome book… and probably most useful for A) data science folk concerned with delivery and communication and/or B) business folk who want to be more data-driven.

Heard about this book from a fellow Instructor. First chapter is really valuable explanation of possible "client" personalities/perspectives/roles that I highly recommend. All the chapters have solid information and explanations. Really good overview book. We recommend it to our Undergraduate and Masters students.

I would say that this book does a good job an overview of different techniques and problems you would encounter, but it does so at a high level. This feels more like for a first year introduction to data science for someone in school or for a non technical data person that is trying to switch into technical portion. Its very hard to find clear books on this topic, where the person breaks it down simply. Gutman and goldmeier do a great job of that, but I think its great breadth but not as much depth. Its still one of the better books out there, but i would not say its worth the price to buy the book.

I recently graduated and started a new role as a Product Manager. I was looking for something clear and comprehensive to complement and improve my data analysis skills at work. This was it!Two reasons I particularly love this book. It is really great at: 1) Identifying and breaking down the most relevant topics.2) Teaching application for real life scenarios.If you dread reading long, mundane textbooks, and you want to learn more about all things data, this book is for you!

Amazing book. Great capture of *almost* everything you should know about data science

In a world where data continues exponential growth trajectory one needs to be able to make sense of the data. Many times people take data visualizations and statistical reports at face value. If only there was a book to help people cut through the noise of Big Data, Machine Learning, and Artificial Intelligence. The book “Becoming A Data Head” is that book that helps readers cut through the noise and hype of these new realms of leveraging data!The book provides tools and details on how readers can better understand the merits of machine learning, artificial intelligence, and overall use of data. There are also chapters on probabilities, statistics, and regression equations that help set the foundation for the more advance methodologies. I love how the authors break down these complex ideas so that readers can be skeptical and challenge assumptions. In addition readers will know how to ask better questions at the start of a project, so that the data analytics project will provide meaningful results.This book will help people work more efficiently with data scientists and communicate the problems they are trying to solve with data available to them. I highly recommend this book for anyone that wants to improve their understanding of data and the methods that can be used to analyze data sets.

First and foremost, I know the main author well, and I'm happy to see this book finally "hit the shelves". It's a book I would've greatly appreciated as an undergrad in statistics (or even graduate school for that matter), as it discusses a lot of practical aspects about being a good data scientist that my old formula-heavy stats and consulting books tended to gloss over. The authors have a natural talent in explaining complex topics to non-technical audience without all the fluff, and have obviously put a lot of thought into the examples used throughout the book (just take a look at the deep learning chapter!). The book covers a lot of important ground and the material is presented well. I think practitioners, whether in academia or industry, will especially appreciate the chapter on common pitfalls, which highlights several common problems we often run into in applied work. Parts I and II of the book (Thinking/Speaking like a Data Head) will help build skills that are critical to applied data science (e.g., asking the right questions and thinking about variation). Whether you're an experienced data scientist, or just getting started, I wholeheartedly recommend this book to you.

What is special about the book is the sequence in which the author chose to present his thoughts. While this book is no means a guide to implementing a data driven strategy or applying machine learning in a commercial setting, this is definitely a helpful book for people who have enough hands on experience in implementing end-to-end analytics solutions and getting ready to try their hand on a strategic leadership role. Treat this like a cheat sheet to avoid pitfalls and you will be able to appreciate the presentation format.