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Appearing For A Data Science Interview? Be Prepared For These 7 Types Of Questions

Appearing For A Data Science Interview? Be Prepared For These 7 Types Of Questions

Srishti Deoras

Appearing for data science interviews can be a dreadful task for many candidates due to the fear of the unknown in the interview process. To acquaint data science enthusiasts, especially freshers, we even cover a weekly feature on data science hiring processes in different companies. It aims to give them an insight into the skill sets required, the growth opportunity, along with the educational background that is required and most importantly, the kind of questions asked. Based on our past interactions with the companies we are listing seven types of questions that data science candidates should be prepared for

Puzzles

Usually designed for freshers entering the field, puzzles are the warm-up interview questions that tend to analyse a candidate’s approach towards solving a problem. Moreover, it tests the logical and analytical aptitude of the candidate, which are the key criteria for a job in the analytics and data science domain. Many tech companies such as Google and Microsoft include puzzles and riddles as a part of their interview process. Below are some puzzles that data science freshers can prepare for their upcoming interviews.

Interesting Puzzles To Prepare For Data Science Interview

10 Commonly Asked Puzzles In A Data Science Interview



10 Standard Puzzles Asked During Analytics Interviews

Guesstimation

Guesstimation questions during the data science interview proved to be useful in order to understand a candidate’s aptitude in reasoning and get a sense of estimation based on the information available. Guesstimation involves a lot of evaluation and studying data to consolidate the result, which is the underlying work of data scientists on a day to day basis. There is no right or wrong answer to these questions, but the interviewer tries to understand if a candidate has got the context of the question. Some of the common guesstimation questions are: 

  • How many people wear blue in New York on a typical Monday?
  • How many tennis balls can you fit into an aeroplane?
  • How many laptops are sold in India every year?

Mathematics Questions

As the name suggests, these questions are aimed at analysing a candidate’s quantitative aptitude since data science is all about data and numbers. Starting from simple and basic mathematical questions around percentage and ratio to topics such as linear regression and probability interviewers may throw a wide range of questions. Again, while the interviewers might not be expecting results with exact decimal points, but an understanding of concepts and formulas. These questions are asked more at the entry to mid-level hiring to check a candidate’s ability to validate results, which is one of the important tasks that data scientists have to perform regularly. 

Programming & Coding Questions

These are the most important Type of questions at almost all the levels of data science interviews. Data science job requires proficiency in programming languages such as Python, R, Java, C++, SQL, among others. Candidates may be asked to programme for a particular problem during the interview in any of the languages that they have learnt or worked with in the past. Programming and coding is the key to implementing algorithms, and with these questions, interviewers will test a candidate’s familiarity with the technical tools and their comfort with it. There are many books and free online resources available for candidates to get an understanding of what kind of questions around coding may be asked in a data science interview. 

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Statistics & Machine Learning Questions

Aimed for candidates from mid to senior-level, these questions revolve around statistics and machine learning and are aimed at understanding the subject-level knowledge. It may include questions ranging from a wide range of topics such as neural networks, GANs, k clustering, among others. While statistical questions are the critical foundation of a data science job, machine learning questions are aimed more at understanding how a candidate solves complex real-life problems by using the concepts in ML. It cannot be denied that a deep understanding of machine learning is a must for data science jobs. Some of the questions are:

  • What is the difference between the Type I error and Type II error?
  • What is the minimum no. of variables required to perform clustering?

Case Study & Scenario Questions

It is extremely essential for data scientists to have worked on the real-world problems, which is where questions around real-life scenarios come into picture during these interviews. Candidates are often asked about the problem from a case study they have worked along with questions like, how they approached a particular problem in their previous job, what were the challenges they faced, how did they overcome the challenge, etc. 

Apart from asking questions from the case studies that a candidate has dealt with in the past, they may also be charged with questions around a specific scenario to find the practical understanding of the tools and techniques. These questions analyse how a candidate envisions a particular problem and works toward delivering results from start to finish. It is important that a candidate related to how technical knowledge can help with business outcomes while explaining the thought process behind the approach. 

Behavioural Questions

Lastly, these questions are aimed at understanding a candidate’s soft skills and if they are going to be a cultural fit for the organisation. Some of the questions asked are ‘what you liked, disliked about the previous position’, ‘what made you switch jobs’, etc. which are intended to understand if the job role at hand suits a candidate’s temperament and personality. While analytical abilities are important for data science roles, companies look for candidates who are passionate and are a cultural fit in the organisation. 

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