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How to prepare for a Data Science Interview?

Do you have a data science interview coming up and want to know how to prepare for it? If so, you have already made some progress. Getting an interview means you have gone through the initial stages of the hiring process. This includes applying for a job and submitting your resume. You may also have taken a technical and aptitude test. Given that you have been selected for the interview, you would want to be well prepared to impress your potential employer. Given below are a few tips that will help you crush your data science interview and land the job you aspire. 

data science

Have a good idea of the specific position you are applying for and its job description

Data science roles are still relevantly new, and job responsibilities can vary significantly across companies and industries. Make sure you know the job responsibilities and skill requirements of the specific position you are applying for. You should have most of the skills required, and you should be willing to learn the ones you don’t. For instance, if you have learned Python in your data science course but the company you are applying to uses R, you will learn it. 

Look at the job requirements to form an understanding if you would be well suited for the job. Is web scraping and writing web crawlers by inspecting web pages something you will be able to do? Do you find text analysis through the use of different NLP modules exciting? Do you like writing queries and extracting data from NoSQL and SQL databases? Leverage your interests and strengths to give yourself the best chance at success. 

Review your resume before appearing for the interview

With most interviews, you will be asked about your background at first and how you are qualified for the job. Being prepared for these questions will allow you to set the tone for the rest of the interview. You won’t make a good impression if you fumble for answers. It is also good to use this time to calm your nerves before the technical questions start. 

Furthermore, reviewing your projects is essential, and you should stay prepared to talk about your project and the data science processes you have used for designing it. It would be best if you were prepared to answer why you use the specific tools, what you learned during the project and what problems you faced along the way

Go through past interview questions

This is particularly applicable to big companies. If you are applying for a job at one of these companies, they would have interviewed other people previously. Many people share their interview experiences on online platforms, including the questions they were asked. Reading about these questions and solving them gives you an idea of the kind of questions you might be asked. Even if you cannot find previous interview questions for the company you are applying to, solving other companies’ questions will still be helpful as they are likely to be similar or correlated. Not just the data science questions, it helps you be prepared for the behavioral questions as well. 

Don’t be afraid of asking the recruiter

Your only contact at the company you are applying to is the recruiters. You should ask the recruiters about how your interview will be structured, what should be your uniform for the interview and which resources you can use to prepare for the interview. You can even try getting to know who will be interviewing you and find out more about them. 

Mock interviews are always helpful

Interviews tend to be nerve-racking, particularly when you have to deal with technical questions. Try to get mock interviews from individuals who’ve been through the whole deal. It will help you to be more prepared for what’s to come. Even if you do not find someone to take mock interviews, you can still practice by trying to solve questions on a notebook or whiteboard. This will help you get used to writing algorithms in places other than the code editor. 

It is a good idea to develop the habit of answering questions related to the constraints and scope of any problem you might be solving. Asking questions now and then is a good way to know if you are on the right track. Getting the right answer isn’t what it is all about, and the right answer usually doesn’t even exist. It is all about how you approach problems and how well you work with others. 

Practice the required skills

You need to base your preparation on the job description and the information you gather from the recruiters. Focus on the topics that are most likely to be in the interview. You can find questions on specific topics online and in books. Review your skills in statistics, programming, and machine learning algorithms. 

  • Excellent programming skills are required for some positions. As such, you may be asked questions that are typically asked in a software engineer’s interview. You should expect that if you are applying for a position that demands programming skills. 
  • For some positions, great statistical skills are required and you should understand various population distribution, hypothesis testing, A/B testing, and experimental design. 
  • Your communication ability will be tested for most data science positions. You should be able to communicate why data science matters. Data science is eventually used for helping make informed business decisions for increasing profitability and efficiency. So, for any problem, you should solve it keeping in mind the business part of the process. 

Send a follow-up email

A standard etiquette after appearing for an interview is to personally send a thank you email within 24 hours. In your email, you should thank them for the opportunity and express how enthusiastic you are to work for a particular company. 

If you do land a job after your first round of interview, congrats! However, as it happens in most cases, you might be turned down. In that case, use your previous interview experience to your advantage and prepare for your future interviews accordingly. 

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