Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street PDF AZW3 EPUB MOBI TXT Download


Authored by two Ex-Facebook employees, Ace the Data Science Interview is the best way to prepare for Data Science, Data Analyst, and Machine Learning interviews, so that you can land your dream job at FAANG, tech startups, or Wall Street.What’s inside this 301 page book?201 real Data Science interview questions asked by Facebook, Google, Amazon, Netflix, Two Sigma, Citadel and more — with detailed step-by-step solutions!Learn how to break into Data Science, with tips on crafting your resume, creating kick-ass portfolio projects, sending networking cold emails, and better telling your story during behavioral interviewsQuestions cover the most frequently-tested topics in data interviews: Probability, Statistics, Machine Learning, SQL & Database Design, Coding (Python), Product Analytics, and A/B TestingEach chapter has a brief crash-course on the most important concepts and formulas to reviewLearn how to solve open-ended case study questions that combine product-sense, business intuition, and statistical modeling skills, and practice with case interviews from Airbnb, Instagram, & AccenturePraise 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 is currently a Data Scientist at a Hedge Fund, and previously was a Data Scientist at Facebook working on Facebook Groups. He holds a degree in Computer Science from the University of Pennsylvania and a degree in Business from Wharton. In college he interned at Facebook, Bloomberg, and on Wall Street.About Nick Singh:Nick Singh started his career as a Software Engineer on Facebook’s Growth Team, and most recently, worked at SafeGraph, a geospatial analytics startup. He holds a degree in Systems Engineering with a minor in Computer Science from the University of Virginia. In college, he interned at Microsoft and Google. His career advice on LinkedIn has garnered over 25 million views.

Nick Singh
Ace the Data Science Interview (August 16, 2021)
301 pages
English
978-0578973838

File Size: 61 MB
Available File Formats: PDF AZW3 DOCX EPUB MOBI TXT or Kindle audiobook Audio CD(Several files can be converted to each other)
Language: English, Francais, Italiano, Espanol, Deutsch, chinese

  • I like the way topics are organized. It covers almost everything. This book suffers from two major issues: 1- they try to explain some technical concepts as brief as possible but they couldn’t and majority of the technical parts are half backed especially in statistics and machine learning. 2- it looks the authors didn’t have a chance to read whatever they wrote. The text has many grammatical errors, missing mathematical symbols and many other issues.
  • 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!
  • Concepts are solutions in this book are very briefly explained which are hard to understand and need to google the solution of the problems. List of questions are good though. Some people said in reviews that they got the job using this book 1-2 days of release of this book. How’s that possible ?
  • 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!
  • 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 interview.
  • 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 interviews!
  • 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!
  • 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 preparation2. probability3. statistics4. coding and databases5. machine learning6. product sense7. use casesI 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!
  • I read a comment in Linkedin recommendation about this book, get it within 48 hours.In 290 pages, everything about interview tips, knowledge in statistics, database, machine learning you need to know enough for an interview, love the interview questions with model answers for easy, medium and hard. Instead of studying with an ocean amount of materials, it is key and narrow down the scope.
  • I used this book to prepare for a recent data scientist interview. It was very comprehensive and covers many topics including chapters on preparing CV / resume, good portfolio projects, behavioural interviews, probability, statistics, machine learning, coding / data structures and algorithms, SQL and database design, product sense and case studies. Thing I liked the most was the breadth of the book. Only negative I can think of is I would have liked to have seen to prepare is a real worked example of a take-home challenge in the book, although acknowledge where the authors state they were difficult to incorporate into the book due to differing from company to company, being long and needing a real dataset.
  • The book covers a wide range of topics including how to write a good cv, how to build kick ass portfolio projects, how to pass the behavioural interview and how to get the technical skills required in order to get into data science. I really like the fact that the authors give a lot of non technical tips that you would never find anywhere else and that can really make you stand out during the interview process. If you’re looking for a Data Science position, then I strongly recommend the book !
  • Just 4 chapters in and I’ve already used some of the tips and examples for cold emailing to reach out to prominent individuals within the AI industry.And it worked.This book not only provides advice and tips for acing the data interview, but the tips are transferable to other aspects such as networking and marketing.Good read.
  • The book is very helpful to help focus on the right topics in the right depth. One of the challenges of studying data science is knowing how much depth and breadth is required to land a job. This book really helps with that.
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    File Size: 61 MB