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Every Data Science Interview Boiled Down To Five Basic Questions

This article was posted by Roger Huang. Roger Huang heads up growth and marketing at Springboard. He broke into a career in data by analyzing $700 million worth of sales for a major pharmaceutical company. Now he writes content that compiles insights from Springboard’s network of data experts to help others do the same.

Data science interviews are notoriously complex, but most of what they throw at you will fall into one of these categories.

Data science interviews are daunting, complicated gauntlets for many. But despite the ways they’re evolving, the technical portion of the typical data science interview tends to be pretty predictable. The questions most candidates face usually cover behavior, mathematics, statistics, coding, and scenarios. However they differ in their particulars, those questions may be easier to answer if you can identify which bucket each one falls into. Here’s a breakdown, and what you can do to prepare.


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Similar to any other interview, these questions are meant to test for your soft skills and see if you fit in culturally with the company.

           Example: What have you liked and disliked about your previous position?

Always explain the thought process behind your choices and the assumptions that guide them.

The intent here is to identify whether the role you’re interviewing for suits your personality and temperament, and to identify why you’re moving on from a previous position.

Don’t overthink it or imagine that the key here is really any different from any other type of interview: Just understand the role well, avoid talking about issues you’ve had in the past with specific people, and be professional when describing what you disliked and why. A data science role may call for an analytical mind, but hiring managers still want to hear what makes you passionate.


Data scientist roles where you’re expected not only to implement algorithms but also tweak them for specific purposes will usually come with mathematical questions.

Example: How does the linear regression algorithm determine what the best coefficient values are?

The point is to see how deeply you understand linear regression, which is critical because in many data science roles you won’t just work with algorithms in a black box; you’ll actually put them into action. This category of question tests how much you know about what’s actually happening beneath the surface.

So this is one of those “show your work” moments. Trace out every step of your thinking and write down the equations. As you’re writing out the solution, describe your thought process so the interviewer can see your mathematical logic at work.

To read the original article and see the 3 other questionsclick here

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