Plotting data in a graph, linear regression allows us to see the statistical interaction between these two variables, i.e. This includes a dependent variable (the possible outcome, dependent on other variables) and an independent variable (one which we can control and manipulate). “Linear regression is how we explain the relationship between two variables. When faced with a question like this, keep your answer simple and include examples. It is a test of both your knowledge and communication skills. This is a common question that any data scientist should have the answer to.
Here are a couple of examples of statistics questions you might face in a data science job interview: “What is linear regression?” Therefore, expect the interviewer to probe your knowledge. Explaining tricky concepts in simple terms is also key for showing your communication skills. Understanding its benefits and pitfalls is the only way to create accurate predictions (a data scientist’s ultimate goal). Statistics define how we manipulate and read data. It’ll also prime them for any follow-up questions they might have (spoiler alert: they will definitely have follow-up questions!) 2. This will show the interviewer that you know your stuff. If you can, tie your response to the job in question. Touch on data mining, data modeling, machine learning, computer programming, and statistical analysis. This is your chance to prove that you are not one of these candidates.įor instance, discuss the value of data insights to everyone from advertisers to healthcare providers. Despite having laudable skills, some may lack the multidisciplinary expertise needed for certain roles. As a relatively new discipline, data science attracts a lot of ‘wannabes’. This is a veiled way of testing your understanding of the field. Keep in mind what the interviewer needs to know, rather than getting side-tracked (which is easy to do!) “Why is data science important to you?”Īnother straightforward question that is less innocuous than it seems. Whenever possible, link your response to the current role.
Highlight which aspects of data science interest you most, before touching on your practical skills. Have you always worked in data science? If not, how did you get into it? What excites you about the field? This question is perfect for flaunting your passion. Rather than regurgitating what they already know, tell them something new. But there’s more to it than meets the eye! If you’ve made it this far, the interviewer will have already seen your resumé. However, an interviewer will usually be probing deeper than you think: “Tell us a little bit about yourself”Ī classic interview opener. Introductory questions (or ‘ice-breakers’) can appear deceptively simple. Introductory data scientist interview questions
BASIC DATA SCIENCE INTERVIEW QUESTIONS HOW TO
By the time you’ve finished reading, you should know how to turn even the most challenging data science interview questions to your advantage! To make things easier to navigate, we’ve broken this post down into the following categories. Knowing your facts is important, but so is being able to spot probing questions! That’s why we’ve focused on helping you to cultivate the right mindset for showing off your best side. Since data science is a relatively new field, interviewers will rarely expect you to know everything. Regardless of the role, there are several key areas where you’ll need to prove your worth. If you’ve been invited for a data science interview, the questions you’ll face will, of course, vary depending on the position you’ve applied for. There’s no getting around it-interviews are tough, especially if you’re entering a new field for the first time. What are the most common data scientist interview questions, and what kinds of answers are hiring managers looking for? Find out in this guide.