How to Crack the Data Science Interview?- 7 Things to Focus

In order to get a Data Science Job, the most important part is the Interview. So if you are struggling to clear the Data Science Interview, then you should read this article. In this article, I will discuss how to crack the Data Science Interview by just focusing on the 7 most important things.

So without further ado, let’s get started and learn how to crack the Data Science Interview?

How to Crack the Data Science Interview?

Most of the Recruiter focus on these 7 things to hire a Data Scientist.

Now let’s understand these 7 things into more details-

1. Build Your Digital Presence

Now we are in the Digital Age. So, a one or two-page resume is not enough to get an interview call. You need to make your presence digitally. And here I am talking about GitHub Profile, LinkedIn profile, and others.

Most of the recruiters visit your GitHub profile and check what type of projects you have done. So don’t avoid your GitHub profile before applying for a Data Science Job. And make sure all POC projects you have done are mentioned in your GitHub profile.

Whatever projects you mention in your GitHub profile, always create each pipeline starting from Data collection to Model deployment very clearly. That will give a good impression of your work on the recruiter.

The next important profile is LinkedIn Profile. In your LinkedIn profile, mention all your skills, your current job, your roles & responsibilities, etc.

And make many connections as possible with fellow Data Scientists on LinkedIn. If you have a good connection with other data scientists, then there is a possibility you get a referral. But this works mostly with experienced people.

Another thing you can do is start writing Blogs related to Data Science. There are various platforms that allow writing blogs on their website like Medium. Your blogs show that you are giving something to the data science community.

NOTE- Don’t leave this step till the end!. Most people create a GitHub profile a few hours before applying for a job. In a few minutes, you can’t make your GitHub profile attractive. It’s a time taking process to build your presence on GitHub or build a good connection on LinkedIn.

So give your time and commitment to your GitHub and LinkedIn Profile at least 6 months before applying for a job.

2. Make a Clean Resume

The next step is to put all your digital profile links in your Resume. It’s very important to mention your GitHub, LinkedIn, and your own website link( if available) in your resume. Because there is a high possibility that a recruiter visits your GitHub and LinkedIn profile to check what projects you have done.

And that’s why digital presence is most important. So, Resume and Digital Presence are connected and collectively the first step towards cracking the data science interview.

For Resume part, I would like to give some additional tips that will really help you.

Additional Resume Tips-

Your resume should be well formatted and contains all important information like Projects, Skills, and your professional profile links.

The template of your resume should be clean and classic. Avoid templates with so many graphics. It gives a bad impression to the recruiter.

Try to end up on one page because recruiters give only 30 seconds to your resume. You need to impress him/her in a 30-second span of time. So instead of writing a heavy text about your projects, just mention Your Project Title with a GitHub link. And in GitHub, describe every pipeline in detail. I hope you understood.

In terms of skills mentioned in your resume, you need to be smart. That means suppose you have to apply for a job that listed the following skill on the top- “Knowledge of a variety of machine learning techniques”. So all you need to do is add this skill “Knowledge of a variety of machine learning techniques” on the top of your resume.

But…wait!

Do this step only when you really have a Knowledge of a variety of machine learning techniques. Because if you mention this skill on the top but in reality, you don’t have enough knowledge about machine learning techniques, then it will be embarrassing for you during the interview.

So, be honest in your resume, mention only those skills in which you have enough knowledge and you can answer on those topics during the interview.

Read this article before creating a Resume for Data Science- How to make Data Science Resume to Get Hired?

3. Introduce Yourself in Terms of Data Science

So after creating a well-formatted resume and digital presence, you get an Interview Call. Congratulation!. Now it’s time to convert the Interview into an opportunity. And for that, you should react as a Data Scientist throughout the Interview.

The first and mostly asked interview question is- “Tell me something about yourself”. And many people do mistakes while answering this question. By asking this question recruiter want to know about your data science knowledge, not about your personal background.

So, how you answer this question will decide the rest of the interview questions. That means, suppose the recruiter asked you, “Tell me about yourself”. And you said My Name is … and I have done so many Data Science Projects. And in one of the projects, I have done completely from one end to the other end. Starting from data gathering to model deployment.

So, what happened here?. Most probably the recruiter may ask the next question- “How you gathered the Data?” or something related to your project.

And by explaining your project in the introduction, you successfully convert the recruiter’s interest in your project. This will create more confidence in you, because the next question recruiter may ask you will be knowing actually.

That’s why you should take this question seriously. Before going for an interview, create a good answer in your mind for this question- “Tell me something about yourself”.

Use Cases-

The next important thing is in most of the cases recruiter gives you a Use Case related to Machine learning or deep learning and tells you to solve.

So when you are solving the use case in front of the recruiter, make sure you follow end to end cycle. Don’t worry about the data collection part but the rest of the parts like cleaning, feature extraction, model training, etc.

You should also mention which Machine Learning algorithm you take for this problem and why this algorithm.

So, in a nutshell, you should sound like a professional data scientist throughout the interview process. And for that, you should do projects as much as you can. And here the next most important thing comes, that is-

4. Do Projects as Much as You Can

In order to answer confidently during your interview, you should have a good amount of knowledge in Data Science. I am not talking about only theoretical knowledge but practical knowledge.

And practical knowledge comes when you do projects. So once you gain enough theoretical skills by Data Science Courses, just focus on projects. I insist on people focusing on projects in every article.

The more projects you have done, the more confidently you can answer during the interview. Try to take part in the Kaggle Competition. And try to get a rank between 1-100. If you get a rank between 1-100 and put on your resume, then there is a high chance that you will get a call from companies.

While explaining about your projects, don’t miss any important pipeline in the process. That means explain everything to the recruiter starting from data gathering to model deployment. That will give the impression that you actually worked on some serious projects.

And in this step, you may expect some additional questions from the recruiter like- “Why did you use this particular method?”, “Why not another method?”, or “Why did you choose this machine learning algorithm?”, “Why not any other ML algorithm?”, or something like this.

So if you have good command and a deep understanding of the project you have done, then you can easily answer such kind of questions.

5. Gain Good Machine Learning Knowledge

During projects, you would use Machine Learning algorithms. So whatever Machine learning algorithm, you used in your project, make sure you have a complete understanding of this algorithm. You should know the mathematics behind the algorithm.

Along with this, you should have an overall good understanding of Machine learning concepts and algorithms. Because the recruiter may test your Machine Learning skills. So for that, you can enroll in the Andrew Ng Machine learning course or any other good course to learn Machine learning before going for an interview

6. Have good Command in Statistics

As a Data scientist, you must have good Statistics knowledge. Recruiter expects from you that you have enough fundamental knowledge of statistics. Whatever machine learning algorithm you use in your projects, you must aware of the statistical significance of the algorithm.

You can learn statistics with these courses that I have mentioned in this article- Best Course on Statistics for Data Science.

7. Good Programming Knowledge

During the interview, there may a coding round or MCQ round where you have to solve small chunks of code or correct the output. That’s why you should have enough programming or coding knowledge to clear the coding round.

That’s all…

If you focus on these 7 things, then definitely you will crack the Data Science Interview.

Conclusion

I hope now you got an answer to the question- “How to Crack the Data Science Interview?“. If you follow these tips and strategies, then your struggle to clear the data science interview will be no longer. I tried to provide you useful information related to the data science interview.

If you have any questions, feel free to ask me in the comment section. I am here to help you. And If you found this article helpful, share it with others to help them too.

All the Best for your Data Science Journey!

Happy Learning!

Thank YOU!

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Though of the Day…

‘ It’s what you learn after you know it all that counts.’

– John Wooden