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

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A good way of showing your business acumen as a data scientist is to build a portfolio of work. Portfolios are typically viewed as something for creative professionals, but they’re becoming increasingly popular in the tech industry as competition for roles gets tougher. 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 Do your values align with them? Do you like their culture? When briefly introducing yourself to the employer, subtly describe your life principles. Be honest with yourself and select only those values you truly believe in to make the right impression on the interviewers. Find out the company’s recent achievements

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. This sounds obvious, but it’s so easy to forget. This is especially true if you’re a data geek that loves to talk about statistical models and machine learning. Adel Nehme: Now, obviously, outside of the CV itself, a major part of building an appealing data science profile or resume are projects and building a portfolio of projects. What do you think a good portfolio looks like? And what are the principles you recommend here for candidates to stand out? Before your interview, take the time to research the company and understand its mission, values, and industry. This will not only help you tailor your answers to the company’s specific needs but also show your interviewer that you’ve done your homework. Practice, Practice, PracticeHave a look at what products or services they offer. Select two of them and think of the ways you can enhance their functionality using your data scientist skills. During the interview, do not hesitate to share your ideas. Offer the company a few concrete ways your skill set will make it more competitive. By "selling" your value to the company, you dramatically increase your chances of getting hired. Study their competitors Statistics is the other class of problems you might be asked to whiteboard. Somewhat ironically, this is actually the easiest part as complex statistical functions in these languages are generally abstracted into an easy-to-use, one line function.

Do not be late but do not arrive too early. Coming 5-10 minutes before your interview slot is reasonable for most situations.Define pandas library and data-frames. The Pandas library is built on NumPy and provides easy-to-use data structures and data analysis tools for Python. A data frame is the primary two-dimensional data structure in pandas, with columns of different types Adel Nehme: I think one common neutral information that you find a lot of CV's is that hero text introducing an applicant that often reads highly motivated professional seeking position, etc, etc. I think that takes away a lot from a resume, right? If this section felt like a lot of new information, I’d recommend starting with a basic R or Python tutorial. DataCamp has a nice one for R and CodeAcademy has a good one for Python. Data manipulation For a deeper understanding of machine learning, our course on Linear Algebra for Data Science in R is a good primer. Data visualization Algorithms undergird much of the work that you'll be doing as a data scientist. Questions on algorithms are primarily designed to test how you think about a problem and demonstrate your knowledge.

Explain the differences between accuracy, precision, recall and F-1 score. Accuracy is the number of classifications a model correctly predicts divided by the number of predictions made. Precision tells us how many of the correctly predicted cases turned out to be positive (TP/TP+FP). Recall informs us how many of the actual positive cases we were able to predict correctly with our model (TP/TP+FN). F1-score is a harmonic mean of Precision and Recall. Your environment should be well-lit; test the video quality before your interview. Make sure your webcam is at a flattering angle.Adel Nehme: Yeah. It's really interesting. Someone like me for example, I started off as a data scientist, but now since it's at the intersection of marketing and data science, I think only now do I realize the importance of being in someone's inbox and being able to reach them and tell them this is what I'm all about. This creates a strong connection down the line. Kevin Huo: And so really being able to demonstrate that, for example at Facebook, everyone knows that A/B tests are so core and experimentation's so core to the company culture. It's definitely a very attractive selling point. And so that would be the first additional tip. And then I think the second one is also, we touched upon this earlier, but just basically having good, honest job descriptions. So there's that phrase, might be butchering it, but it's happiness is the delta between expectations and reality. So in the same way, a lot of candidates, especially junior ones, they might have these expectations like, "Oh, I'm going to join this company and I'm going to build these ML models that will get this much revenue uplift." Think also about how you managed – or would manage – expectations. It’s well known that business leaders can expect data to be a silver bullet when it comes to results, so how do you make sure that people are realistic. Show off your data science portfolio

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 algorithmsAnother element here is to be able to talk about the advantages and disadvantages of different tools. Why might you use R over Python? Which Python libraries should you use to solve a specific problem? And when should you just use Excel? Spotify Artists Analysis: Analysis and visualization of musical styles from 50 different artists with a wide range of genres on Spotify. As you go through the job description and responsibilities for the position, try to get a clear sense of what will be expected of you. If there is anything in the job description that you don’t understand, search the internet, look up the terms, or call the company and ask for clarification.