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Tips and Tricks to Ace Data Science Interviews – Brand New Podcast Series by Analytics Vidhya!

Introduction

Are you finding it tough to land a data science role? Do you feel fully prepared before you enter the interview room but you just can’t figure out why you keep getting rejected?

I’ve been there. Thousands of fellow aspiring data science professionals have been there. It’s a tough pill to swallow.

Before I landed a data science role, I worked in the learning and development field for over 5 years. I held little to no technical experience, forget data science! So when I started studying up on the various tools and techniques, I did so without any proper plan or structure.

And I kept botching my data science interviews. No matter how much I studied or did certifications, I couldn’t break into this field.

 

The Idea Behind ‘Ace Data Science Interviews’ Podcast

So what was I doing wrong? Did this mean I couldn’t ever land a data science role?

Certainly not! But most data science aspirants give up early or don’t have anyone to guide them. That’s a major reason Kunal Jain, Analytics Vidhya’s CEO and Founder, decided to launch the ‘Ace Data Science Interviews’ venture, including a brand new podcast series and a comprehensive end-to-end course!

Kunal has worked with several companies over the last 10 years to set up their data science teams. He has also helped millions of people learn about data science careers through Analytics Vidhya. Who better than Kunal to guide you through your own journey?

In this weekly podcast, Kunal will share his experience about data science interviews. Each episode will cover topics like what works (and doesn’t) in data science interviews, ways to perform better in these interviews, among other things.

 

Each episode will cover a certain topic and take a question from you, the audience. Reach out to us at [email protected]!

The podcast is available on the below platforms:

The first 3 episodes are already out! This article is my understanding and summary of each episode. Make sure you subscribe and listen every week. We look forward to hearing your thoughts and feedback!

 

Episode #1: Top 3 Mistakes Data Scientists Make in Interviews

Learning a topic means accepting you’ll make mistakes. The key is learning from them and taking appropriate action. The same applies to data science. It’s a vast and complex field so it’s inevitable you’ll make some mistakes in your journey.

However, we have noticed most people making the same common mistakes. I covered them in details in my article here, and Kunal picked his top 3 based on his own experience conducting these interviews. Let’s go through them individually:

 

1. Not Thinking in a Structured Manner

When people come across case studies, guesstimates and puzzles during their data science interview, the first instinct is to jump the answer. There’s not much thought behind how to structure your thoughts – a big no-go for an interviewer.

Let’s understand this with an example. Suppose you have recently joined a transport company as the CEO. The company has been posting heavy losses recently. How would you go about turning around the situation?

In Kunal’s experience, people start listing off ideas, like “analyze the pricing”, “look at the overall costs”, “look at the route planning”, etc. That is the absolute wrong way to go about things! Consider this a rejection for sure.

Lay down a framework using pen and paper (or a whiteboard). Kunal has explained how to do this for our case study in this episode. Putting a structure to your thoughts showcases your thinking ability – a must-have skill in data science.

 

2. Not Communicating your Thought Process with the Interviewer

Focusing too much on the answer and not on the process is a sure shot way to failing your data science interview.

Think about it for a moment – the aim behind an interview is to help the interviewer understand the thought and reasoning behind your problem solving skills. Right? The interviewer does not care much about if your answer is precise to the decimal point.

There might not even be a right or wrong answer in the first place! So make sure you communicate your thought process to the interviewer, including the assumptions you are making. It’s a win for both sides.

 

3. Expecting only Technical Interviews as part of the Interview Process

There are several layers to a data science interview. The process isn’t one-dimensional! Just preparing for questions around tools and techniques will not land you the role. Of course these technical skills matter, but there are other equally important topics you will be judged on.

There will be an interaction with the project team, a case study related to the domain, role plays, and much more. You should prepare for all these formats.

We have put together a 7-step framework for understanding and acing each of these different interview rounds in the ‘Ace Data Science Interviews‘ course. Head over there and check it out!

 

Episode #2: 3 Game-Changing Tips to Ace Data Science Interviews

In this episode, Kunal shares his thoughts on 3 uncommon yet immensely useful steps you can take to ace your next data science interview. Most people won’t mention these tips, but they can be game-changing in your journey!

 

1. Share Updates on LinkedIn Regarding your Recent Data Science Projects

One of the best ways to showcase your knowledge is through projects. This is especially true when you’re a fresher or coming from a non-data science field. We encourage you to share updated on LinkedIn about your recent side projects, or blogs, or GitHub repositories.

Take feedback from data science influencers or subject matter experts. Data science is still a small community and these folks are typically very happy to provide their thoughts if you ask appropriately.

How will doing all this help land a job? Well, in our experience and discussions with data science recruiters, they admit they go through a candidate’s LinkedIn profile (including recent activity) before the interview. Sounds like a great opportunity to show-off, right? 🙂

 

2. Ask High-Quality Meaningful Questions to the Interviewer

An interview is meant to be a two-way discussion. We are no longer in the “Q and A” era where you sit and rattle off answers to cliched questions. At least not in data science. These interviews give you a chance to assess the company and the interviewer.

Candidates still don’t use this opportunity as much as they should! They either don’t ask any questions, or keep the questions limited to topics like work timings or the team size. This shows an inability to think outside the box and worse, a lack of curiosity. A big red flag in data science.

Do your research about the company and the project beforehand. Prepare questions and try to pry as much information out of the interviewer as possible, such as the company’s past performance, future plans, your role, etc.

 

3. Don’t Try to Fumble Out Answers for a Topic you haven’t Studied Before

This is a very common mistake people have. There will be certain questions you won’t know the answer to. That’s ok – it’s human to not know everything. But candidates still try to answer these questions by making up answers on the spot. This isn’t a great look.

Let the interviewer know that you aren’t an expert on the topic. Highlight a way to solve the problem which you already know. For example, if you don’t know how a boosting algorithm works, you could solve the same problem using a technique you do know. And later, learn boosting and get back to the interviewer.

This shows two important things to the interviewer:

  • Your ability to solve a problem by looking at different methods
  • Your willingness to learn and apply yourself

 

Episode #3: Top 4 Ways to Showcase Real-World Data Science Projects

A common question we come across in our community – how do I showcase my data science skills if I don’t have any previous work experience? Every interviewer demands it, and no fresher can seemingly grasp it. A classic catch-22 situation!

Here are 4 ways to showcase your experience on real-world projects, even if you don’t have previous industry experience:

 

1. Get an Internship in a Data Science Organization

Internships are a very effective way to break into data science – even for experienced people. We have personally seen so many successful transitions enabled by internships. The bar for an intern is slightly lower than that for a full-time employee.

You should absolutely check out this comprehensive guide to cracking your first data science internship. It is full of tips, tricks and resources to help you prepare for the interview process and the actual internship.

If you are looking for a guided journey with mentorship – check out our Certified Program on Data Science for Beginners (with Interviews) . This program will complement your foray into data science and give you a huge advantage in your internship search.

 

2. Participate in Data Science Hackathons and Competitions

Hackathons and competitions usually provide industry related problems and challenges. So you not only get to work with these datasets, you can also gauge your standing among the top data scientists globally. It’s a tremendous learning.

You won’t get a high score everytime. In fact, the first few times might be brutal in terms of where you finish. But don’t give up! The key is in learning from each competition and improving the next time you participate. Eventually, your hard work will pay off.

We regularly host hackathons on the DataHack platform. So go ahead and enroll there. There are plenty of FREE practice problems and datasets there as well. Practice, practice, practice!

 

3. Attend Meetups and Network

Meetups are a great way of meeting new people from various domains and expanding your network. Attend as many meet-ups as you can in your city. We have seen plenty of people form teams at these meetups and solve problems using data science.

 

4. Identify Problems you Face and Try to Solve them using Data Science

Quite often, people struggle to stick to a domain. There are so many datasets available online that it becomes tempting to pick one at random and try to solve it. While I like the enthusiasm, it’s not a great way to build up your own learning.

The problem becomes useful when you add your own context to it. That will enable you to truly understand what’s at stake. You would be aware of the depth of the problem and can decide which technique to use when. It’s a powerful way to learn data science.

So pick a problem that you have personally faced, or perhaps a recurring problem in your domain. As an interviewer, I would definitely be impressed with the proactive action.

Kunal has illustrated this point using a few awesome examples. Make sure you listen to this episode!

 

End Notes

We are absolutely thrilled to be going on this new venture with you. Successfully clearing data science interviews is a challenge many have faced, and yet only a few have cleared. We hope this podcast, our course, and our platform will help you land your dream data science role.

Below are the resources we mentioned in the article. Make sure you subscribe today and we’ll see you around:

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