Heard In Data Science Interviews: Over 650 Most Commonly Asked Interview Questions & Answers

First, I'll say I've only looked at the first chapter (CS fundamentals) and second chapter (prob/stats) up to this point. There are seven chapters in total.==== PROS ====You can use this book as a way of sampling different concepts to recognize whether you're familiar with them. Some questions essentially serve as flashcards, e.g., "What is X concept in [comp sci / stats / ML]?" This can be useful. Even if it isn't in "flashcard" format, you can treat it that way (e.g., "okay, this is asking about the binomial distribution...maybe I'll review that later").==== CONS ====The main issue is that *many* answers to the math-y questions are incorrect.Here's the hit rate as I'm working through chapter 2:2.1: This question is famously ambiguous and deceptive (there's a full Wikipedia about it being a "paradox"), so the answer key giving a one-liner for the answer without addressing its ambiguity is strange.2.2. Fine2.3. Fine2.4. Incorrect...and it's also a brainteaser that isn't related to probability or stats. The question is well-known, and other information needed to solve the problem. This isn't caught because only a rough sketch of how to solve the problem is given as the solution.2.5. Good. Well-explained.2.6. This asks for a generalization of the previous question and is much more complex, but the answer (if it's correct) is given as a one-liner. Explanations can be found online.2.7. So simple it doesn't seem to fit with the previous questions.2.8. Good.2.9. Brainteaser that has nothing to do with probability or stats.2.10. The answer is correct, but the math getting to it is incorrect. There's a missing term on the left side of the only explanatory equation.2.11. Incorrect. You can google the answer. (It was used as a question on a midterm exam at CMU and is also correctly solved elsewhere on the internet.)2.12. Has missing information in the question that isn't addressed as explicitly as it should be in the answer.2.13. Incorrect. A Bayesian stats question where some of the information needed is not given. The answer therefore doesn't make sense.Also, the computer science question quality is inconsistent, and code answers are given in C++ instead of a more common (and less verbose) data science language. I'd definitely use "Cracking the Coding Interview" instead of this book for coding stuff---although the flashcard-y questions still might be useful.

I found this book very useful for gauging the breadth of my knowledge and finding holes. However, as many other reviewers mention, the book is full of mistakes. There are lots of grammar mistakes, but more importantly many of the answers are incorrect (conceptually wrong, not just typos/careless errors). Personally, I find it fun because I can play spot the error, but you definitely need other sources of information to actually learn topics you haven't seen before.There are also a couple bad questions (either unclear or demonstrating the questioner doesn't really understand), but I think you'd expect that from an interview as well, so that can actually be useful in practicing how to respond.

The book provides a decently organized but VERY comprehensive collection of the majority of the DS questions that you'd get in an interview. The book does a good job of covering MOST things, which is the greatest value of the book -the comprehensiveness, hence my 4 stars. One downside is that the answers are inconsistent in how helpful they are, and some of them are flat out wrong (as others have noted). I often find myself using the internet to further supplement the answer section to bolster my understanding. Also, the CS coding answers are in C++, if that matters to you. Python would have been preferable for me.

I don't find this book by no means sufficient enough to prepare you for data science interviews.There is no depth in the answers. Often the answer to a probability problem, for example, is just a number without any details about the thought process.I also find some answers to be at least vague, if not wrong. If for every question I have to stack overflow the answer in order to understand it or verify it's correct, then definitely doesn't worth my 50$.Finally, just a heads up, all CS examples are in C++ (not sure if it's mentioned somewhere in the description, but was expecting Python given that it is specifically for data science).

As Data Scientist interview covers a broad spectrum of topics (and most of us don't work in all at any given point), I was looking for a book that briefly covers all topics for interviews.This book turned out to be a great find.I found the book well written, easy to digest and informative. Answers are clear and helpful.The book covers most popular data/AI topics.Questions on Computer Science Fundamentals (Chapter 1), Machine Learning (Chapter 3)and NLP (Chapter 5) turned out to be the most relevant for my interest. Unlike other similar books, this one has no gotcha questions. Highly recommended.

It has been useful to me, but it definitely could be better. The solutions are very terse and don't help you much if you're too dumb to understand.

Succinct question and answer format so one needs to look into things more and not just rely on this. But the questions are helpful covering basic to intermediate machine learning and distributed system concepts.

The book consists of all interview questions in the beginning and has solutions in the back. I can't comment on interview questions, but the solutions are sometimes very short without much explanation or incorrect. It may be a good reference book for the night before the interview.

The questions are very basic and not all of them come with answers. Don't expect any problems or intermediate level questions. They are almost exclusively junior level, theoretical questions: What is gradient descent, Why is Naive Bayes naive, what is softmax regression etc... I would give 2 stars for someone putting the effort to collect the information in one place, but then again the price is ridiculously high for what it offers; you get very advanced textbooks around that price.

I am not sure how come this book costs so much??? A collection of questions you can find on the web if you know what you are looking for. Not for a Data Scientist, not for a Programmer, not for a Big Data Engineer, not for a System Designer. Just a frustrating piece of junk.

not a good book, answers are in C++, some answers say explain urself