{"id":65542,"date":"2019-07-31T11:23:29","date_gmt":"2019-07-31T05:53:29","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=65542"},"modified":"2025-08-04T21:44:46","modified_gmt":"2025-08-04T16:14:46","slug":"data-science-interview-preparation","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/data-science-interview-preparation\/","title":{"rendered":"Expert’s Tips to Prepare for your Upcoming Data Science Interviews"},"content":{"rendered":"

Data scientist job interviews might seem difficult to deal with. These interviews may not be like any other field. Those who take your interview are the technical interviewers who can even assign you work in the first meeting!<\/p>\n

Without getting afraid, you need to solve the given problems. For this, you need to prepare yourself<\/p>\n

To know what are the data science skills that you need to have, you must check out the article Top Data Science Skills<\/a><\/strong><\/p>\n

So basically, there are 3 different positions for a data scientist. The first one is for beginners or entry-level position, the second one is for an intermediate or mid-level position, and the third is for an expert or the advanced-level position.<\/p>\n

This blog on data science interview preparation is curated keeping in mind all three positions.<\/p>\n

(Note: For the future purpose, you can even bookmark this blog! You might need these data science interview tips)<\/span><\/strong><\/em><\/p>\n

How to Prepare for a Data Science Interview?<\/h3>\n

We will discuss this section in two parts – preparation before any data science interview and tips during the data scientist interview. So, let’s start –<\/p>\n

Preparation before a Data Science Interview<\/h3>\n

1. Whether you are applying for an entry-level position or an advanced-level, you need to practice the most asked question \u201ctell me about yourself\u201d<\/strong>. You don\u2019t have to prepare a 10-minute speech on this, but a 1-1.5 minute pitch would be enough.<\/p>\n

2. Go through your resume and public profiles<\/strong> like LinkedIn, Facebook, Stackoverflow, Kaggle, etc to revise all the work (projects and internships) you have done.<\/p>\n

3. Practice coding<\/strong> for a few days, regularly, for an hour probably. Read all the questions before you start coding, this will allow you mind to start working in the background.<\/p>\n

4. Learn about the basic concepts other than that of data science such as Machine learning, Deep learning, and Natural processing language. For this, check out DataFlair Free Tutorials Library for Data Science<\/a><\/strong><\/em>.<\/p>\n

5. Prepare yourself with the basic statistics related questions<\/strong>. Even if you don\u2019t have statistics degree, focus on basic questions such as assumptions of linear regression, type 1 vs type 2 error, hypothesis testing, etc.<\/p>\n

6. Before you go for the interview, you should know about the company<\/strong> and what kind of work they do. Do good research, go through their public profiles, and read about their requirements. They will probably ask you \u201cwhy you wanted to apply to this company\u201d.<\/p>\n

Tips one must Follow while appearing for Data Science Interview<\/h3>\n

1. The interviewer is ALWAYS interested in <\/span>your past work<\/strong>. If you are a beginner in the field work, he will ask you about the projects you were involved in. And if you have experience in a company, explain your role properly, give details of the work you have done there, and tell them what you learnt from the company (if asked).<\/span><\/p>\n

2. The interviewer will ask you questions related to the company\u2019s domain (the one which you are being interviewed for). He will do this to see if you can apply your knowledge conceptually<\/strong> to the problem. Make sure you understood the problem clearly and act upon accordingly.<\/p>\n

3. If you are able to answer the problem-solving question, there are definitely going to be some follow-up questions<\/strong> depending on your answer such as \u201cWhat kind of data will you need? How will you get it?\u201d etc.<\/p>\n

You can consider these top data science interview questions<\/a> beforehand to have an idea about the kind of questions that can be asked. It has more than 250 interview questions.<\/strong><\/em><\/p>\n

4. The interviewer will judge you on the basis of your attitude and behavior towards the different scenarios given to you at workplace. He might ask you questions such as \u201cHow will you handle a conflict situation with your boss? Do you prefer to work in small teams or large teams or by yourself?\u201d etc.<\/p>\n

5. The interviewer will not just ask you questions related to work but also about what you do at home. He might ask you \u201cwhat blogs\/websites do you follow to stay in touch with the latest technologies?\u201d You should follow the data science community and be connected with people who are in the same field and for latest updates related to the latest cutting-edge technology you can visit DataFlair on a regular basis<\/em>.<\/p>\n

6. Ask questions. An interview should be a mutual session, where both, interviewer as well as the interviewee asks and answers. Politely ask questions such as \u201cWhat are the projects others are working on right now? What languages do they prefer to use? Can I expect growth in the role?\u201d etc.<\/p>\n

Don\u2019ts of a Data Science Interview<\/h3>\n

1. Poor Communication<\/span><\/h4>\n

If you are able to freely engage in a good conversation with your interviewer, you are most likely to achieve the goal of having the job. Communication is what it takes to be successful in the field of data science.<\/p>\n

As you are going to involve in presenting your data analysis with your team, you are required to have good communication skills to do it. Moreover, you are obviously going to speak more than your interviewer and you are the one who is going to direct the interview.<\/p>\n

2. Not Asking Questions<\/h4>\n

\u201cThe one who asks a question might seem a fool for five minutes, but the one who does not ask any question remains a fool forever\u201d.<\/p>\n

In data science interviews, a good interviewer will always provide you an opportunity to ask questions at the end. Do not hesitate! This is the time to ask away whether this new job is a good fit for you. This helps the interviewer to assess your seriousness to work.<\/p>\n

3. Being Overconfident\/Underconfident<\/h4>\n

Talking about things that you don\u2019t have complete knowledge about is just too embarrassing. Once a candidate jabbered about artificial neural networks (ANN)<\/strong><\/em> for 25 minutes straight during an interview.<\/p>\n

After that he realized that he has always worked on logistic regression and did not have a good understanding about ANN. And worse was, he was the one to start a conversation on it.<\/p>\n

Of course, he did not clear the interview for being way too confident. Only talk about things you are comfortable with, it makes a good impression.<\/p>\n

4. Not Giving An Explanation To Your Answer<\/h4>\n

Don\u2019t give answers like yes or no to questions that require your part of explanation. The better you explain your answer, the more the interviewer will find you fit for the job. Try to explain why you gave such an answer, go into details, connect cues (interviewer might help you with this).<\/p>\n

For data science interview, go well prepared with one of your favorite projects and make sure to know every small detail about it.<\/p>\n

5. Using Incorrect Phrases<\/h4>\n

Here are 5 phrases that are inappropriate to use in a data science interview:<\/p>\n