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1. A True Software Engineering Guide for Data Scientists
   I have been meaning to write a review for a while now. To give you an answer if you should or should not buy this book as a Real Guide to Software Engineering as a Data Scientist, my answer is (YES, YOU SHOULD BUY THIS BOOK!). My background is straightforward. I graduated with a BS in Electrical Engineering (Electronics and Telecommunications), where I was mainly on the hardware side of engineering, then transitioned to an MS in Electrical Engineering (Specialized in AI), where all my coursework and projects were in Artificial Intelligence, where I learned how to be a Data Scientist and an AI Engineer, so you can see I skipped Software Engineering. The moment I knew that I struggled with these essential skills was during my first post-master’s job as an AI Consultant, where I was having a lot of issues with production-ready code, large codebases, testing,…etc.This book saved my career. It made me more of a software engineer, which, in my opinion, should be the foundational skill before entering the data science field. Now, back to the book: I found it easy to understand and follow. It was insightful about which tools to use, when to use them, and how to use them during the coding and scripting process of production-ready projects.The only issue I have with the book is not with the author but with the publishing company O’Reilly. Some books are colored, and some are not, which I find inconsistent with publishing best practices.

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2. Great book!
   Heard about the book on the MLOps podcast and decided to buy it which overall enjoyed!

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3. Decent for a beginner
   I’ve been a data scientist for about 4 years and have worked with a lot of colleagues that are terrible at coding and best practices in DS. I really was hoping this book was more in depth. If someone is new to data science and does not know what a linter is and is a poor programmer, this book is a good one to read. Unfortunately I didn’t learn anything from this book so it’s targeted to beginners or people learning DS.

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4. Extremely clear, great way to brush up or learn the fundamentals of SWE for Data Scientists
   This book will give you a very clear exposition of key SWE concepts to improve your coding as a data scientist, and to better understand how to work with other software engineers. It won’t teach you everything you need to know to be a SWE or become an expert python programmer. The concepts are laid out clearly, and intuitively with a lot of care to explain why, not just what. The examples are also easy to follow and illustrative, and are very DS oriented.

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5. Initially came across this book through an early release, yet the full book experience was somewhat lacking. I was expecting this book to be a companion to data scientists transitioning to ML engineering roles, but it was a bit more like a collection of tips for improving code and project quality. To give the author full credit, it is a well structured book with a lot of helpful examples. If you already have extensive applied experience, you would probably want to read the DevOps for Data Science book.

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