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VERIFIED VIDEO REVIEWS ABOUT JEFF LERNER!
Meet Mark Tomaszewski from Montana. This 59 year old real estate investor and re-modeling contractor has been an entrepreneur his whole life but only recently dove into the online world with his training here at The ENTRE Institute. With commitment, consistency and support from the ENTRE team and community, Mark was able to overcome many challenges in learning a totally new skillset. He’s proud to say he’s seeing big financial gains and he knows this is only the beginning as he continues to grow as a person and a business professional.
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Sam was a child refugee from Iran. He was raised by his mother, who fled to the US in 1985.
Opportunities weren’t handed to him.
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Sam had to work for everything he has accomplished today.
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With the mantra, “success without fulfillment is the ultimate failure,” his dedication and mindset are a true inspiration.
Today, with his credentials and experience, Dr. Bakhtiar has helped over 100,000 people transform physically and mentally. He specializes in helping people get to the top 1% in any and every aspect of their life.
Sam applies his business acumen and coaching to The 1% Club, helping others to become a 1%er by rising above.
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Mark Tomaszewski Reviews
Mark Tomaszewski Reviews
Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street
Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street
by Nick Singh (Author), Kevin Huo (Author)
4.5 out of 5 stars
The article was written by two ex-Facebook employees. , Ace the Data Science Interview is the most effective way to prepare is to be prepared for Data Science, Data Analyst is to be prepared for Data Science, Data Analyst, and Machine Learning interviews, so you are able to land the dream position at FAANG or tech startups and Wall Street.
What's in this 301 page book?
The 201 authentic Data Science interview questions asked by Facebook, Google, Amazon, Netflix, Stripe, Two Sigma, Citadel and many more -- along with detailed step-by-step answers!
The most frequently asked questions cover the following subjects in interviews with data: Probability, Statistics, Machine Learning, SQL & Database Design, Coding (Python) and Product Analytics and A/B Testing
Each chapter is a quick review of the most important concepts and formulas to be reviewed
Learn how to tackle open-ended questions in case studies that mix business intuition, as well as statistical modeling skills. Practice using case-based interviews taken from Airbnb, Instagram,& Accenture.
Learn how to get into Data Science, with tips on how to craft your resume and portfolios that are kick-ass as well as sending cold email to networking contacts and better telling your story in behavioral interviews.
Praise for Ace the Data Science Interview:
"The advice in this book directly helped me land my dream job"
-- Advitya Gemawat, ML Engineer, Microsoft
"FINALLY! Cracking the Coding Interview but for Data Science & ML !"
-- Jack Morris, AI Resident, Google
"Super helpful career advice on breaking into data & landing your first job in the field"
-- Prithika Hariharan, President of Waterloo Data Science Club; Data Science Intern, Wish
"An invaluable resource for the Data Science & ML community"
-- Aishwarya Srinivasan, AI & ML Innovation Leader, IBM
"Solving the 201 interview questions is helpful for people in ALL industries, not just tech!"
-- Lars Hulstaert, Senior Data Scientist, Johnson & Johnson
"The authors explain exactly what hiring managers look for -- a must read for any data job seeker"
-- Michelle Scarbrough, Former Data Analytics Manager, F500 Co.
About Kevin Huo:
Kevin Huo works as working as a Data Scientist for an Hedge Fund, and was previously a Data Scientist for Facebook involved in Facebook Groups. He has a diploma from Computer Science from the University of Pennsylvania and also a Bachelor's Degree from Business at Wharton. In college , he was an intern for facebook, Bloomberg as well as at Wall Street.
About Nick Singh:
Nick Singh started his career as a Software Engineer in Facebook's Growth Team and, more recently, he was employed at the SafeGraph, the geospatial analytics startup. He has a degree in Systems Engineering with a minor in Computer Science from the University of Virginia. While in college, he worked with Microsoft in addition to Google. His career tips have been read by more than 10 million users on LinkedIn.
Portfolio Project & Cold Email Advice Was Gold
Reviewed in the United States on August 23, 2021
The chapter on portfolio projects gave amazing tips that helped me create the perfect project that I could add to my resume and talk about in interviews! The chapter on SQL was great in helping me go over the basics and the questions helped me review for my upcoming data analyst interview. Would highly recommend for anyone looking for data related career advice!
Must-Read Before Your Next Tech Interview
Reviewed in the United States on August 23, 2021
The statistics and machine learning questions explained in this book directly showed up in my data science interviews at Google! I'm so glad I read this book before going for those interviews
Great Crash Course On Data Science and ML with plenty of interview practice!
Reviewed in the United States on August 31, 2021
Warning: This is NOT a good book to learn Data Science or ML from scratch. However, it IS a great resource to quickly review the most relevant topics in the few short weeks before an upcoming Machine Learning or Data Science interview, and also anticipate the types of questions companies ask. Where the book really shines is the real interview questions. For example, there are 40 statistics interview questions from companies like Google and Amazon, and 35 Machine Learning questions from companies like Spotify and Citadel. Plus, there are non-trivial open-ended case questions too, which do a great job of combining all the various facets of real-world scenarios. Highly recommend this read before a technical intervi
Great for Aspiring Data Scientists & Professionals Alike
Reviewed in the United States on August 25, 2021
This book provides not only a great coverage on various really important topics that could surface in a data science interview, but also really practical tips that would be applicable for technical interviews in general. I also love that in addition to getting a refresher on various topics, the book also provides a LOT of practice questions that will come in handy when I prepare for interviews. Overall, would recommend to anyone looking to prepare for data science interv
Great book for DS interview preparation.
Reviewed in the United States on September 9, 2021
I am using the book to prepare for interviews. I found it very useful. I have read the ML chapter so far. i think one needs to have a good foundational knowledge for the topic to use the book more effectively. I did notice some typos but it is very minor compared to the great usefulness of the book. Highly recommend it!
8 people found this helpful
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The only one of its kind - highest recommendation!
Reviewed in the United States on October 24, 2021
First, let's get one thing out of the way: Data Science is tricky. It's translating business questions, requirements, and needs into actionable insights. It's designing and interpreting the result of data-driven experiments. It's machine-learning and A.I. It's statistics. It's math. It's everywhere, and it's hard.
While there are no shortage of books out there that seek to aid the prospective product manager or software developer in preparing for interviews in their respective fields, this is the only book in its class for data scientists that covers what you'd need in terms of:
1. behavioral interview preparation
2. probability
3. statistics
4. coding and databases
5. machine learning
6. product sense
7. use cases
I purchased it one week before my technical interview with a large social media company, and I was able to move to the final-round interviews using the insights in this book. And that's only after having read the (6) and (7) above.
Nick and Kevin deserve a lot of praise because a lot of the material in the book would be totally inaccessible to candidates without any experience in some tech/social media. In this book, you'll find contextualized (and some not so) practice questions for FAANG companies as well as finance, and Wall Street. The material is invaluable for this alone.
If I could make a recommendation based on my interview journey thus far, it would be to include material that deals with the shapes of real-world distributions i.e. "what do you think the distribution of time spent per day on Facebook looks like?”
Overall, top-notch, highest possible recommendation!
11 people found this helpful
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It's like "Cracking the Coding Interview"...but for data science & ML!!!
Reviewed in the United States on October 18, 2021
This book is the perfect resource for those who have recently completed, or who are finishing up, a data science-related academic program, boot camp, or self-directed upskilling regimen.
It's also a great guide for those with a few years of experience who are ready to start interviewing for more senior DS/ML roles.
In short, if you have upcoming interviews in the datasphere, you should treat this book like oxygen...because it's what you need to survive and thrive where you're going!!!
The first four chapters deal with basics that you'll need to nail in order to captivate the attention of recruiters & hiring managers (e.g., nailing your resume, nailing your project portfolio, etc.).
The next 7 chapters will set you to work grappling with specific interview questions from a range of "holy grail" employers about probability, statistics, ML, SQL & DB design, coding, product sense, & case studies.
Example Question: "Uber - Explain the Central Limit Theorem. Why is it useful?"
👆 The authors go on to provide a thorough solution (in this case multiple paragraphs that take up ~half of a page).
Notably, all 201 interview questions covered in the book include thorough solutions (hence the 290 pages!).
BOTTOM LINE: If you want to be competitive in data science interviews at FAANG-level companies - or utterly dominate data science interviews at average companies - then, this book should be your training ground of choice!
24 people found this helpful
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A must purchase book for data science interview prep!
Reviewed in the United States on August 23, 2021
I've just started reading this, and the first few chapters already have lots of interesting advice that I never would have thought of myself. In addition to specific interview question/solutions on statistics, ML, SQL and coding, there are a good amount of general interview/job hunting tips as well.
I'm excited to see what else I can take away from
Everything needed for data science / ML interview prep in one place
Reviewed in the United States on September 18, 2021
Finally, no more having to read random Medium articles! I found this book super useful because it contains everything you'd ever encounter in the data science interview process of pretty much all tech companies.
There's also a great primer on machine learning that covers the important basics that are useful to know. I also loved the collection of real interview questions, accompanied by solutions - probably also one of the most valuable selling points of this book. After going through the questions I felt pretty confident I'd be able to pass any data science interview. I say this w/ confidence, having myself worked for years at a FAANG company and having many friends (besides myself) that have gone through these interview loops.
4 people found this helpful
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Plenty of practical advice that extends beyond the 201 questions
Reviewed in the United States on November 4, 2021
tl;dr
Great advice for preparing for Data Science interviews. It will help you understand (i) what you should do to get an interview (ii) the structure of the interview, (iii) the type of questions asked and how to solve them, and most importantly (iv) how to formulate your answers in a clear and effective manner.
The first three chapters of the book provide sound advice on the things you should do before you even start applying for your dream job. These are (1) how to fix your resume, (2) how to go about building a portfolio of interesting projects, and (3) how to increase your chances of getting noticed by using cold emails.
Chapter 4 discusses the "behavioral interview", which in fact is part of any interview. This chapter is very short, but carries valuable insights and equips you with tools to think about how to structure your answers most effectively.
Chapters 5-9 are filled with questions and answers on the various aspects of a technical interview (probability and statistics, machine learning, SQL, and coding). The questions in each chapter are grouped by level of difficulty (easy, medium, hard), which is very helpful. The technical questions are great for understanding the type of questions asked during interviews, and moreover, help you realize the areas in which you should study more.
The last two chapters, 10-11, discuss product sense and case studies. This is a great resource and one that is often neglected in the standard learning regime for an interview.
I got the book when it came out, as I was already in the process of scheduling interviews. Overall, while I didn't have the time to complete all the technical questions, reading the book helped me throughout the different interviews. Specifically, the End-to-End ML Workflow in chapter 7 helped me to formulate my answers and excel in questions like "Here's a problem. How would you go about solving
Comprehensive and to the Point
Reviewed in the United States on November 5, 2021
1. Authors get down to the point early into the book about the job search process. However I wish this part of the book was expanded upon a bit more.
2. The second part of the book (bulk of the content) is on the technical interview portion. This part goes over at a high level each area of data science and the core technical knowledge you need to know. I'm actually having fun reading these chapters as they help you see the forest and how each concept is applied.
The only negative is that someone without a prior familiarity with data science (not the primary target audience of this book) would have trouble following some of the chapters.
2 people found this helpful
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Super helpful, even for those of us with over a decade of experience
Reviewed in the United States on February 2, 2022
Although I've been working as a data scientist since before "data science" was a buzzword, I was recruited for a data science position in "big tech." I wasn't necessarily looking for new opportunities when the recruiter approached me but I was interested. I normally approach interviews with some prep about the company, the role, and polishing off my resume and originally, that's how I approached this interview. Eventually, however, I learned that a lot of "big tech" companies have a significant breadth of knowledge that they cover in their interviews so I started digging into what I could expect.
The initial recruiter screen followed by the first technical phone screen were straightforward. After being informed that I was being moved on to the final round, I wanted to explore more of what my knowledge may have been missing. I don't remember how I discovered this book (likely through some blog about "beating the [company] interview!") but "Ace the Data Science Interview" ended up being tremendously helpful.
Much of the content was a reminder of what I needed to study, things I had forgotten since undergrad, especially in the probability and statistics chapters. The example questions in these chapters were great, putting me back in the mindset of test taker, making sure I understand the question, before diving in. The chapter on product sense was the most helpful for me as it codified a lot of the work I've been doing in recent gigs. Although I'm not a product manager, much of the product sense chapter helps non-PMs start to think like a PM and that chapter was a great primer to that portion of the interview.
Was this the only resource I used to get an offer? No. There's a lot of prep required if you want to do well in a knowledge-based interview. Of the resources I used to prep for my interview, was this the best one? Yes. I put together study guides, I read and researched, and I bought domain-specific books, but "Ace the Data Science Interview" was hands-down the best resource I had in prepping for this interview.
One person found this helpful
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Reviewed in the United States on October 13, 2021
I bought this book a week ago, and I have learnt a lot by just going through couple pages, things which was never told before. This book is a perfect combination of theory and practically needed to hone your skills as well as to enhance your soft areas that will give you a competitive edge.
The author really cares about others to succeed personally and professionally and is also available through his LinkedIn to talk about topics, ideas or points that can be added in the book.
Definitely recommend anyone who is making the transition or trying to expand your horizons in the data science world
3 people found this helpful
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Great resource for aspiring and current data scientists to prepare for interviews
Reviewed in the United States on September 7, 2021
The questions and solutions are all well written and very practical!
Very Organized book for practising different areas of interviews
Reviewed in the United States on October 15, 2021
1. This book is a really good place to practise different areas of the DS interviewing process. Instead of scouting glassdoors for specific interview questions, I referred to this book which saved me a lot of time.
2. Good for timing yourself and practising real interview questions. I like the fact that the questions were divided into easy-medium-hard so that I got to assess myself regarding where I stand and go back and revise the areas that need work.
Strongly recommend.
3 people found this helpful
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Reviewed in the United States on September 3, 2021
This book offers a very comprehensive review over the common data science topics. It really helped me to obtain a clear and structured view of how data science skills are evaluated in interviews. The problems and solution at the end of each chapter is also very good resources for interview preparation.
4 people found this helpful
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Go-to Book For Data Scientists Trying To Advance Their Career
Reviewed in the United States on August 28, 2021
Great cheat sheet of high level concepts to review before interviews! It's nice to have a bunch of practice questions with solutions all in one place instead of having to search the internet for problems.
This book is an absolute MUST HAVE!
Reviewed in the United States on November 30, 2021
This book is an absolute must for students or professionals who plan to make a transition into Datascience. It’s easy to get lost with too many DS & ML jargons and not know a thing about it. This book does just more than that. Right from providing tips and tricks on having a kickass resume and project portfolio to heavy Stats, probability and ML concepts, everything is covered in this book.
After reading this book, I have personally gained a lot of confidence in making a transition into DS coming from a software engineering field.
I’d definitely recommend this book to any student or a professional trying to make a switch into DS. 👍🏻
Good collection of interview questions and technical refresher
Reviewed in the United States on August 31, 2021
I enjoyed the thorough collection of questions from real interviews. The probability/statistics/machine learning exposition is a good, high level refresher - the reader should not rely on it for learning the concepts. The 'how to get a job' section may help those newer to the job hunt. A few minor typos and errors here and there, but I'm sure those will be corrected in a later edition.
3 people found this helpful
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Helpful for Data Science & Data Analytics Interviews
Reviewed in the United States on September 22, 2021
My roommate and I both bought copies of this book before our on-campus job interviews. The book does exactly what it aims to do - provide 201 commonly asked interview questions with solutions, along with brief walk-throughs of the most important sub-topics like SQL, DB Design, product sense, and hypothesis testing. While I wish some things were covered more in depth, at 290 pages this still is way more in-depth than anything out there when it comes to interviews.
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