How to Lead in Data Science

This book is an excellent stage-by-stage guide for data science practitioners at all career levels. The chapters are well organized and ordered, and the authors do a fantastic job of breaking down complex concepts. I found the chapter-end summaries very helpful for overviewing chapter content and allowing the reader to perform a self assessment. I also really enjoyed the sample case studies and scattered gem insights throughout the book - they made the concepts easy to digest. I strongly recommend this book for anyone looking to improve their understanding of data science!

I like the book a lot, it approaches the topic in a systematic way. By leverage the different personas at different levels of career growth, the authors pointed the potential mistakes and growth areas that the data professionals need to focused on. It describes the guidelines in terms best practices from IC to executives. As one of data professional practitioner, I feel I can directly applies some of the techniques and approaches in my work. Many of the guidances resonate with me as I reflect my own journey of data professional careers as IC and manager/executive. The back of book chart summarized book key points into one chart, which describe the virtues & capabilities for both IC and management individuals at different levels. Definitely an excellent book. Highly recommended. It will be a book that I will constantly go back to and reference in my future work.

This is a very well written book on how to become a leader in data science field. While there're many books on data science applications, this is the first book that systematically provides career guidance for data science professionals at different career stages: tech lead, manager, director, executive. The authors provided concrete case studies based on real life examples to illustrate their points. I especially like the table at the end of each chapter to self-assess against the virtues areas discussed and the template to plan the actions to improve the needed areas. Every data science professional could use this book as a reference to advance their careers.

Leadership is required for every data scientist because a fundamental part of data science work is to influence the larger organization using data, which requires deep soft skills. Unfortunately, it is hard for data practitioners to learn those skills, arguably due to the lack of structure in data science education. This book provides a field guide for data scientists at all levels -- from a project lead to executives -- to grow the crucial soft skills. The authors provide a solid framework based on their experience of leading data science teams in multiple companies and give a lot of concrete examples. The framework is highly applicable and effective, the book itself is super easy to read. Spending a few afternoons on it would fundamentally change your data career. I would highly recommend this book to all people who want to become good data science leaders.

TL:DR This book leans into the concepts of designing good data products and good data teams. It does not teach algorithms, how to make friends, or the hottest tool right now.Notice: I won this book at a drawing during a meetup session with the authors. I was not paid to write this review. This is my opinion as a data professional for 20+ years.This book has a zen-like structure and readability. The first place of this is right on their table of contents; parts by persona and sections by skills. Why is this helpful is you'll never feel lost once you understand that the authors establish this path. If you open to a part and section that you feel pertains to your position now and it's too easy, flip further into the book. Too hard, flip to the previous parts and same section. I can't say it's a balanced tree but it sure felt some kind of tree!In terms of the content, when you are an expert of a subject, explanations of what you know is fluid and easy. When you are a master of the subject the explanation is simple, memorable and applicable at any stage. This book is the later. The stories in the book are IRL examples from both author's journey as examples of the concepts. There aren't many books so crafted and told in such a genuine tone to help curious data professionals but if you're not sure I recommend going through the tables and Figures of the book before you read. The tables are the sections "in a nutshell" and deserve to be printed and tacked on to any whiteboard.

It's an essential field guide to helping data science professional navigate these coming critical times for all of humanity. Like it or not, data informed intelligent systems are going to be the backbone of decision making for society. So it's critical that everyone at different career ladders are guided correctly and conscientiously so we all can be proud of what we helped built. I can't think of a better way than to understand all the issues and pros/cons faced by practitioners at each level. It can be frustrating for junior members to not fully appreciate that what got you 'here', won't be get you 'there'. This book is a handy reference with actual examples to prevent such misunderstandings for all the stakeholders.

This book was a great resource on several dimensions.1) Many organizations struggle with agile adoption because of a lack of the fundamentals around design-focused thinking and planning before executing. This is the first book about data science that makes the connections between agile techniques and data science in an effective way that enables teams to plan work, execute and measure the outcomes.2) The approach of using career paths for various roles and experience levels makes the content easy to follow and actionable regardless of where someone is in their career.3) The book is full of solid recommendations on different data science techniques to apply by problem domain.