Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street

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What is the Central Limit Theorem? It depicts that when we have a large sample size, the sample mean should be normally distributed, so the sample mean will be tending to the mean of the population and the sample variance will be equal to the variance of the population divided by the sample size regardless of the distribution of the original population. This is when you will be tested on your core data science knowledge and general technical knowledge. This can be something like a data challenge that you have to complete over a week or two. When interviewing for more advanced roles, you might get asked about machine learning or statistical modeling. HR Interview R. Practice some Machine Learning Interview Questions in R. It may be challenging, but it is a great learning experience! Questions cover the most frequently-tested topics in data interviews: Probability, Statistics, Machine Learning, SQL & Database Design, Coding (Python), Product Analytics, and A/B Testing

Adel Nehme: Exactly. And I'd love to pivot here maybe to discuss the hiring manager perspective instead. Now of course, I'm sure preparing for this book meant that you've also spoken with a lot of hiring managers who've been hiring data scientists. I'd love to know, given your close work with them, what do you think are some of their best practices hiring managers need to adopt when hiring talent and what are some of the biggest pitfalls they should avoid? Ask for more time if the question requires it. It shows that you take their questions seriously. However, do not do it for every question. Explain the role of a data scientist If you’re applying for a job as a data scientist you’ll probably already know the answers to all of these. Just make sure you have a clear answer and that you can explain each in a concise manner. Know your algorithms A strong portfolio can set you apart from the competition. Showcase your best projects, highlighting the impact your work has had on real-world problems. Be prepared to discuss each project in detail, explaining the challenges you faced, the techniques you used, and the results you achieved. Be Ready for Behavioral Questions

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But, as we’ve seen, that’s foolish. To ace a data science interview you can’t just recite information and facts. You need to talk clearly and confidently about your experience and demonstrate your drive and curiosity.

An absolutely fantastic in-depth tour de force on data science interviews (and more importantly, getting the interviews themselves!). I particularly enjoyed the end-to-end ML interview questions and answers at the end of the book. While others cover more general data science topics any aspiring data scientist will likely need to know at an interview.

Kevin Huo: The same way, for these younger candidates, on the job descriptions or maybe when they join your firm, just make sure that you're willing to talk with them and just, "Hey, here's how you could have more and more impact at the firm. And where do you want to go?" So it's also about, again, it's such a hot job market these days, it's not just about, "Hey, I want to work for you." It's also about like, "Hey, how can you grow the candidate's career as well?" Kevin Huo: And if you can make that mental connection that like, "Hey, this person is a really smart, really hungry, just wants to learn a lot," we think that it's worth giving them a shot at that. Wear nice comfortable clothes. You want to feel comfortable while also reflecting the company culture and expectations. If it’s a corporate role, consider going smart. If it’s a trendy tech company, you can tone it down a bit. One of the biggest challenges for data scientists is dealing with incomplete or poor quality data. If that’s something you’ve faced – or even if it’s something you think you might face in the future – then make sure you talk about that.

As I alluded to above, the best advice I can give you is not to spread yourself thin by trying to learn everything. Every data science interview I’ve participated in, as interviewer or interviewee, allowed the candidate to solve problems in either language. So ultimately learning either is totally fine. Here are some general guidelines for how to make your choice.You've landed an interview for your dream job as a data scientist and are ready to show off your knowledge and expertise to the hiring manager. But, as a data-oriented professional, you know that the best way to improve your chances of success is by preparing in advance with practice questions and answers. Presenting your findings is an essential part of a data scientist’s job. To do this effectively, you’ll need to translate your findings into laymen’s terms, use charts and visualizations, and be able to explain technical concepts to non-technical stakeholders. So make sure that your data visualization skills are up to par before you walk into your data science interview. The Salary Discussion Data science is a complex and multi-faceted field. That can make data science interviews feel like a serious test of your knowledge – and it can be tempting to revise like you would for an exam. Can you name some supervised machine learning algorithms and the differences between them?” (supervised machine learning algorithms include Support Vector Machines, Naive Bayes, K-nearest Neighbor Algorithm, Regression, Decision Trees) I mean that in two different ways: on the one hand it’s a role that demands a variety of different skills (being a good data scientist is about much more than just being good at math). But it’s also diverse in the sense that data science will be done differently at every company. That means that every data science interview is going to be different. If you specialize too much in one area, you might well be severely limiting your opportunities.

Not having a specific skill is normal. If the company asks for a solution in R, but you only know how to do it in Python, demonstrate how you can solve problems with Python and show your willingness to learn R. Think before answering There’s never been a better time to launch a career in data science. The number of data science jobs is estimated to grow by 30% this decade, and it’s also one of the most lucrative tech roles, with median salaries for data scientists being around a hundred thousand dollars a year.Prepare differently for various types of interviews: phone, video call, in-person, with HR, management, or data professionals This review of AtDSI (gonna need a short version of the book name here!) is from the perspective of the hiring manager, where much of my experience related to DS/ML interviewing comes from. I will comment on how this book can help candidates, but mostly on how it could assist hiring managers. Kevin Huo previously worked as a Data Scientist at a Hedge Fund and at Facebook 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 on Wall Street, at Facebook, and Bloomberg. Kevin Huo: But in more of like, "Hey, this is what I'm exploring, this is maybe the sets of models I'm running together in this particular space," or using whatever data sets, that's going to be more strategic again, higher level thinking of, how does it solve a problem rather than, "Oh, hey, I ran a model and here's what it outputted." No one cares about that anymore in the future with AutoML and all these other innovations.