2024-04-11 Thursday - Book Review: Cracking the Data Science Interview

[image source: Amazon.com]

 

Cracking the Data Science Interview: Unlock insider tips from industry experts to master the data science field (Feb 29th, 2024)
https://www.amazon.com/Cracking-Data-Science-Interview-industry/dp/1805120506/

 
by Leondra R Gonzalez (Senior Data & Applied Scientist, Microsoft), and Aaren Stubberfield (Data Scientist, Microsoft)

[Link to my review on Amazon]

Review Title:
Packed with valuable guidance: A balanced survey of Data Science with great breadth and depth

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 - ARE 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, 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.