Cracking the Data Science Interview: Unlock insider tips from industry experts to master the data science field

This is my first-ever review on Amazon but I had to submit one after this book not only helped me step up my data science skillsets, stand out from the field, and land my first interview - but it also helped me land my first job! I purchased a few other books initially and I could have saved a lot of time and money by starting with this one. It's really clear, concise, and easy to understand - but more importantly it's effect. If you're searching for a book to help you get your dream job - your search is over, read this book!

Cracking the Data Science Interview is the go-to guide for anyone aiming to make a mark in the data science field. With its clear explanations of essential topics such as Python, SQL, machine learning, and deep learning, this book equips readers with the knowledge to stand out in the competitive job market. It's more than just a technical manual; it also covers the soft skills needed for job hunting, like resume writing and portfolio building, ensuring you're well-rounded for any opportunity.The book is thorough, offering insights into the latest trends and demands in the industry. It walks you through scripting with shell and Bash commands, version control with Git, and even data visualization for effective storytelling. With practice exercises and real-world examples, it builds a strong foundation in statistics and modeling before diving into the complexities of neural networks and AI.For those about to face interviews, it's an invaluable resource. It doesn't just teach you how to code; it prepares you for the intense questions and scenarios you'll encounter, so you can approach interviews with confidence. And once you land the job, it helps you negotiate the compensation you deserve.If you're serious about a career in data science, whether you're just starting out or refreshing your skills, this book is an investment in your future. It's detailed yet accessible, full of practical advice and technical know-how, guiding you through every step of your data science journey.

Review thoughts:- It is difficult for most authors to strike the necessary balance when writing a book that covers so much ground - but this book achieves this quite well.- This book is well written - and earns the accolade I reserve for just a few books: Crisp!- The content is very well structured- The authors approach to teaching is actionable - with concrete skill building examples.- This book provides a good outline for helping people identifying gaps in their skills/knowledge- There are great suggestions for the reader to further explore various topics (versus overburdening the focused goals of the book)- Chapter-3 is a fast paced introduction to Python - and provides concise examples to gives the reader immediate skills in writing Python code.- One of the most important techniques the book teaches is covered in the section "Applying scenario-based storytelling".- Chapter-9's coverage of Feature Engineering is noteworthy for being well done in conveying the concepts with easy to understand examples.- The illustrations are very nicely done.- code examples are concise, focused, and well explained.- The "when to use" and companion "tips" sections are very nice touches - that help the reader understand not just the WHAT and HOW, but also the WHY.- The "Assessment" and companion "Answer" sections are a great teaching technique to challenge the reader - and provide immediate guidance to clarify/correct any potential misunderstandings.- In Part-3, the discussion of "Assumptions", "Common Pitfalls", and the associated "Implement Example" entries - IS WORTH THE PRICE OF THE BOOK ALONE.- Any manager or developer - will benefit from using this book's broad survey of topics - to expand their understanding of Data Science concepts and techniques.- As an architect, I learned quite a bit of useful Data Science concepts/techniques by working my way through this book.- If someone carefully worked their way through the full contents of this book - I believe they would have a good foundation established in preparing for a Data Science interview.Suggestions for the next edition:- Create a "Data Science Awesome Jobs Board List" GitHub repository, as a companion to the book.- Add a new chapter to discuss common anti-patterns in data science.- Performance trade-offs/considerations would also be some very important information to perhaps consider adding in a next edition.- An Appendix of Suggested Reading/Books might be helpful (for example, in chapter-3, p-59, while text mining and NLP are noted as outside of the scope of the book - it is an important area of Data Science - and it would be helpful for the next edition to include some suggested books on topics that are designated outside of the book's scope).- On page-331, in addition to the mention of Terraform, it would be helpful to also mention the recent open source fork of Terraform - OpenTofu.There is one critical caution missing in "Part 3: Exploring Artificial Intelligence", "Chapter-11 Building Networks with Deep Learning" (for example, on page-317, in the section: "Introducing GenAI and LLMs"):Any discussion of GenAI __MUST__ caution on the very real risks of hallucination and confabulation.

I've been a practicing DS for 10+ years. I got this book to fill up DS knowledge gaps and pick up some interview tips and tricks.As the book notes, DS means different things to different people. This book does a fine job of covering the major topics that people associate with DS (Querying SQL, Inferential Statistics, ML models and deployment, etc), as well as suggesting resources for readers who would like to delve deeper into specific topics. For the topics that the book does cover, I found the explanations easy to understand. For instance, I've found discussions of the central limit theorem lacking over the years, and found the lenght and depth of the discussion on it to be just right in the book. The related interview questions and answers per topic provided are simple and to the point."Cracking interviews" books usually only focus on how best to answer common interview questions. I enjoyed that this book dedicated 1-2 chapters to delving into the specifics of finding a job in DS, particularly the emotional and time challenges. As folks navigate the process of looking for a job, keeping the advice provided in the book may help them keep realistic expectations and remain resilient.The book also covers topics that I've found are part of a daily life of a DS, but rarely get discussed. For instance, scripting with shell in linux. I probably would not expect a DS candidate to exhibit proficiency with scripting in an interview, but I'd like to get a feeling that the candidate does exhibit some basic knowledge of it.Finally, there are topics that I've unfortunately never experienced in my career such as deploying ML models, so getting a comprehensive overview of the topic was interesting.