Job interviews are scary. Think about the first time that you took a test, got on stage for a theater performance, or were the focal point of a group conversation. Well, job interviews may be even scarier: at their very core, they are asking you to demonstrate you have value to add to their organization. You need to showcase your ability to join a company’s ranks.

Data science interviews can be even scarier than the typical job interview.

My initial foray into data science interviewing was brutal. I was a senior at the University of Washington and, at the time, knew nothing about interviews. My first interviewing experience was a horrible coding call with a VP of engineering at Evernote who stayed silent as a monk on the other end of the phone throughout the entire process.

Fast forward 3 or 4 years. As I started interviewing others for data science roles, I realized the problem I had back then wasn’t really a lack of knowledge; it was more so a gap in terms of what people thought mattered to study and prepare for the interview and what really mattered to ace it.

To avoid this gap, you need to build and follow a researched study plan and get as much interview feedback as you can.

Build a Study Plan

James Clear once said: “In the gym, if you experience no stimulus, your muscles won’t grow. If you step under 10,000 pounds, your body will break.”

Studying for data science interviews is very similar.

We are in the business of building brain muscles to solve very specific problems. Practicing bite-sized interview questions on a continual basis allows you to slowly level up and build on top of your existing knowledge. You can only solve medium-level questions once you can ace easy questions, and so on for hard questions.

Additionally, one thing we’ve learned at Interview Query is that the more problems you study, the more likely you are to pass an interview. This intuitively makes sense but is also backed up by data.

We looked at exit survey data from members on Interview Query and saw a correlation in goal achievement and the number of questions studied:

I will add a slight caveat that, as a data scientist, this is a slightly biased analysis. You would expect that candidates who study a lot of questions are probably more diligent in other areas of their interview preparation (or life) as well and naturally have better outcomes.

But this is still a good representation of how important maintaining effort is towards achieving success. And I’m not going to lie; if anyone studied over 100 interview questions, you would think that they are probably going to do well on their interview.

However, as we previously stated, choosing which questions to practice is actually half the process. So this is what you need to do:

1 - Benchmark your current skills in data science.

Understand where you are stronger and where you are weaker. - Use the courses in this learning path to see which questions are easier for you to tackle and which are harder for you.

2 - Understand the data science market,

Know the necessary skills for the job you want. We already mentioned a few ways in which you can do this: - Ask your recruiter. - Look at Interview Query’s company interview guides and understand the skills they expect. - Read interview experiences online to understand what interviews went like for other candidates.

3 - Practice the problems within the areas you need to strengthen:

• Use this learning path to learn and understand the topics.
• Practice the questions within this learning path.
• Once you have completed a question topic that was hard for you, you can continue practicing by searching for questions that are tagged with that topic in Interview Query’s practice questions.

Keeping up with this process will be much easier if you build a daily study plan. Don’t try to get everything done in a week. You’ll most likely be unable to, and you’ll get tired immediately. You will also struggle to retain the knowledge you so quickly acquired.

Instead, divide what you want to accomplish into a medium-term plan, in which you assign a portion of every day to practice for data science interviews. In fact, this approach is much better for memorizing different topics, as spaced practice gives much better results for fixing concepts in your mind than “binge studying”.

Get Interview Feedback

It’s hard to grind and study for your data science interviews without getting any feedback. Continual feedback is what makes doing tasks much easier because we’re receiving insights into the work you’re doing and allowing you to experience incremental improvement.

Mock interviews and coaching are great simulations for actually testing your live performance against another person. If that person is specifically a data science coach, they can dive into your interviewing process and illuminate exactly how you can improve in certain areas. This targeted feedback will help you to develop the skills you need in order to communicate your ideas and solutions effectively.

Additionally, many interviews aren’t like practice problems since they include problems that slowly build on top of your existing answers with interviewer feedback. When you answer a case question on your own, it’s a lot harder to iterate and adapt.