This should be required reading for anyone that lays claim to being a data scientist. Of all of the books and classes I have taken I found this 94 page book to be the most informative and insightful. Not a book about technology but a book about how we need to think about and make decisions with data.

This book is no longer than it needs to be and the title for 1 of it's 6 chapters is 'Causality' - how could I NOT like this book!

I highly recommend this book to anyone who is involved in the development of software products. This is because above all else, it's book about critical thinking within the context of product - and even more specifically, how to use Data to improve our products.

This book sits in a sweet spot of being high level enough to keep the content flowing as well as peppering it with pin point examples that succinctly illustrate the author's point. The author doesn't waste words overemphasizing points or tying concpets to any specific engineering or project management discipline. This should be appreciated as it respects both the reader's intelligence and time.

If your a product manager, engineer, designer...or anyone else involved in creating and growing products, I recommend this book to you.

Here is an excerpt which conveys my point. This is from Chapter 1 - Scoping: Why Before How:

"...Rather than saying, "The manager wants to know where users drop out on the way to buying something," consider saying, "The manager wants more users to finish their purchases. How do we encourage that?" Answering the first question is a component of doing the second, but the action-oriented formulation opens up more possibilities, such as testing new designs and performing user experience interviews to gather more data.

If it is not helpful to phrase something in terms of an action, it should at least be related to some larger strategic question. For example, understanding how users of a product are migrating from desktop to mobile versions of a website is useful for informing the product strategy, even if there is no obvious action to take afterward..."

Also, Amazon doesn't have a table of contents for this book so here it is:

1. Scoping: Why Before How

2. What's Next?

3. Arguments

4. Patterns of Reasoning

5. Causality

6. Putting It All Together

A. Further Reading

This short book by Max Shron is a good reference to learn about key concepts behind data-driven decision making. One of the most important notion of the book is the emphasis on asking the right question. Indeed, you shouldn’t start with the data, but rather with the question (the problem to solve). The book is full of case studies which are very well described. My only concern is about the number of questions that the author asks, which is maybe exaggerated. In conclusion, Thinking with Data is a short journey that shows formal questions behind data-driven decision making.

"We want to know Y given X. Here's the data. I want a report by Z."

Sounds familiar? If yes, then this book is a must read, regardless of where you sit on the table (analyst or requester). This book is about the importance of collaboration and planning for a data analysis project. Sure. Many businesses will swear that such a process exists, but how well is it working? This book provides useful guidelines and suggestions that can help in evaluating your business's data culture so hat meaningful outcomes are derived that will lead to purposeful action.

Good

This book was suggested as further reading material in a Data Analysis course I took online.

The book has very few pages, but provides lots of useful information and serves as, as the book’s last sentence indicates, “...a clear place to start for every beginner.”

I learned a lot from the book and will be going through it a second time. I did not take notes during this first pass but will do so during the next.

While I found this book to be quite good (to the point and perfect for my needs), I completely agree w/ what 2-star reviews are saying: This is not for people who already have a background in solving problems or advancing products w/ data.

This book is for anyone jumping into data science or a role that requires critical thinking or use of data to solve a problem. But if you already know how to structure problems, how to identify needed pieces of data to reach a solution, or have already successfully managed data-related projects, this book may be a little too elementary for you.

The book touches on basics of scoping a project, making arguments, reasoning, and causality. In the final chapter it applies all that it has taught on 2 realistic use cases and summarizes the process in the last page.

Again, if new to the field, read it. If already seasoned, skip it.

This book is hard to read. I love the concepts but found the explanations confusing. I'm trained in Six Sigma, business analysis and project management and wanted to find something to assist me with a framework for data-related projects. I understand where the author is coming from but the trip to get to the end is just too tough. I stopped reading the book 3/4 of the way through. I'm sure someone will find the book helpful. It just didn't work for me.

This is a very short read. It gets right to the point. The author obviously knows the subject matter is taking on the task of helping the reader apply data without including too many words and fluff.

Probably giving good advice to absolute beginners. For people with experience in data analysis, the treatment of the topic is too superficial and vague to be useful.

I loved this book as a reminder of how to 'think with data'. I recommend all Data Scientist and Business Analysts read this. I love and use every week the CoNVO method. But you'll have to read the book to find out

This will not be your only book about data science but address important and complementary aspects, mainly in methodology, and it does very well. This and the brevity wins all the stars.

Possibly the best idea in this book is the scoping mnemonic convo

Unfortunately I found the rest a little dry and not well supported. Interesting but not great