Increase your chances of cracking the Data Science Interview.
Data science interviews can be a nerve-wracking experience, but with proper preparation, you can increase your chances of success. However, even the most well-prepared candidates sometimes fall into common traps and make mistakes that can cost them the job.
Whether you are a seasoned professional or just starting out in the field, it’s important to be aware of these pitfalls so you can avoid them in your next interview.
In this blog, I will cover 10 of the most common mistakes candidates make in data science interviews(based on my experience of taking over 50 data science interviews) and provide tips on how to avoid them. From lacking basic math skills to poor problem-solving, these are the pitfalls you should be mindful of and strive to avoid. So buckle up, and let’s get started on our journey to ace your next data science interview!
1. Failing to understand the problem statement
Data Science interview involves evaluating the candidates on problem-solving skills. Very often, you will be asked to solve a business problem and explain your approach in detail.
One of the most common mistakes made by candidates in this round is not fully understanding the problem statement and jumping to the solution with their limited understanding.
How to prepare for this? Before diving into the solution, take time to ask clarifying questions about the problem and the data you will be working with. This will help you better understand the task at hand and come up with a more accurate solution.
2. Not preparing for the programming aspects of the interview
Many candidates assume that data science interviews are only about statistics and machine learning, but in reality, they also involve a significant amount of programming skills. Make sure you are familiar with the tools and programming languages, such as SQL, Python/R, etc., required for the job.
How to prepare for this? Practice solving a few coding questions before the interview so that you feel confident during the interview. You can try websites such as leetcode, Hackerrank.
3. Not being able to clearly communicate your thought process
The interviewer wants to see how you think and approach problems. Be sure to clearly communicate your thought process as you work through the problem and explain any decisions you make.
How to prepare for this? Always try to use the STAR approach for answering your questions. You can read more about it — here.
4. Not being prepared for behavioral questions
Many data science interviews include behavioral questions, which are designed to assess your problem-solving skills, communication skills, and ability to work in a team. Many times, the candidates are taken by surprise when they hear these behavioral questions and are not able to come up with satisfactory answers.
How to prepare for this? Go through the list of most common behavioral questions and prepare specific examples from your past experience that demonstrate these abilities.
5. Not being able to express complex concepts clearly
One of the most important skills for a data scientist is the ability to communicate complex ideas in simple terms. Be prepared to explain technical concepts to a non-technical audience, and make sure that your explanations are clear and concise.
How to prepare for this? To prepare for this, it’s important to practice explaining technical concepts in plain language, using real-world examples and analogies to help make the information more accessible. You can seek feedback from friends or colleagues to help refine your communication style and identify areas for improvement.
6. Couldn’t justify the skills mentioned in the resume.
One of the biggest mistakes made by candidates in data science interviews is not being able to justify the skills mentioned on their resume. Some candidates even mention the skills they don’t know just to get their resumes shortlisted.
It is important to remember that the interviewer is looking to see if the candidate truly understands and has practical experience with the skills they claim to have.
How to prepare for this? In order to avoid this mistake, it is crucial to be prepared to provide concrete examples and specific situations where the skill was applied, demonstrating not only that you have the skill but also how you have used it effectively.
7. Basic Math concepts are lacking.
Math plays a crucial role in data science, and being proficient in it is essential for success in the field. This includes having a solid understanding of concepts such as statistics, probability, linear algebra, and calculus.
Candidates who lack these skills will struggle to understand and apply advanced data analysis techniques and may be seen as unfit for the role.
How to prepare for this? It’s important to brush up on these fundamentals before the interview to demonstrate your competence and knowledge in the field. I love Khan Academy videos for brushing up on statistics and probability concepts.
8. Lack of Problem-Solving skills
Problem-solving skills are crucial in data science, and a lack thereof can be a major roadblock during interviews. Candidates who struggle to approach and solve problems in an efficient and effective manner may find it difficult to tackle real-world data science problems and make meaningful contributions to their team and organization.
How to prepare for this? It’s important to be able to clearly articulate your thought process, use critical thinking, and apply logical reasoning when faced with a problem. Solve various kinds of data science case studies available online; you can find a few case studies here — interviewquery.com.
9. Not being familiar with the company and its products
One of the most common mistakes made in data science interviews is not being familiar with the company and its products. Candidates who lack knowledge about the company, its history, values, and offerings can appear disinterested or unprepared for the role. This can leave a negative impression on the interviewer and decrease your chances of landing the job.
How to prepare for this? You should research the company and its products before the interview. It is important to show the interviewer that you are genuinely interested in the company and that you have a good understanding of the company’s business and its products.