Table of Contents

Toggle

4 tips to nail your next data science interview.

One of the most popular questions on r/datascience is “I have a DS interview next week; how do I make sure I’m prepared in time?”

Being a data scientist is hard but what’s even harder is showcasing your skills effectively and accurately.

Most data scientists are highly qualified but lack the ability to put that across to their potential employers, network, interviewees, etc. So, how do you do it?

So, how do you showcase your data science skills effectively?

The first step is to make sure you actually are as prepared as you think you are.

Here is a compiled checklist of technical skills you should have to officially be a data scientist, according to data science central.

Checklist

• Practical-based education

Doing an internship, self-learning online, or learning by solving real-world problems is a far more effective approach when learning data science.

Experience is always more valuable.

This doesn’t mean formal or tertiary education is not important (*ahem, if you’re a student reading this, don’t drop out), but the emphasis on practical hands-on projects will showcase your competency more effectively.

To further elaborate, if you’re in the midst of choosing a formal education within data science to pursue, a related-subject to data science like IT, analytics, maths, or statistics, will give you an advantage.

To qualify yourself further we recommend upskilling by taking online courses. Data science 360 Certification is a great example of a detailed, exhaustive course you can take to empower yourself.

It’s always good to have some background education to make sure you seem like a credible candidate.

Always remember that domain knowledge and experience of the industry is key.

The 3 main components are domain knowledge, programming, and maths.

With mathematics, the common misconception is that a high level of mathematics is necessary.

The truth is that areas of mathematics such as calculus or quantum mathematics aren’t required. Only basic mathematics, high school level, is necessary.

You’re not required to learn advanced maths like algebraic expressions, coefficients, or any engineering-related maths in the field of data science – you won’t be using it anyhow.

One can dive deeper depending on whether or not they get into research.

The purpose of understanding Mathematics in data science is to help in your thinking and problem-solving process.

Knowing which statistical model to use like linear regression, averages, or clustering, to solve a problem is essential.

It is necessary for data scientists to know how to apply these theories than just deriving them.

According to Dr Lau, who is based in Malaysia, “Most data science professionals I’m connected with don’t have mathematics or statistics background.”

• R Programming

Programming is a huge aspect of data science. Being fluent in Python and R is highly recommended. 

• Python Coding

Check out the practical, hand’s on, and easily applicable Python Primer by LEAD for a beginner-friendly course that helps you dive, head first, into your career.

• SQL Database/Coding

SQL is a domain-specific language used in programming and designed for managing data held in a relational database management system, or for stream processing in a relational data stream management system.

• Hadoop Platform

Although this isn’t a necessary requirement, it does make you more attractive to potential employers.

• Apache Spark

Apache Spark is designed for data science to run its complicated algorithm faster.

It helps you disseminate data processing when dealing with a big sea of data, therefore, saving time.

• Machine Learning and AI

If you want to stand out from other data scientists, you need to know Machine Learning techniques such as supervised machine learning, decision trees, logistic regression, etc.

These skills will help you to solve different data science problems which are based on predictions of major organizational outcomes.<

• Unstructured data

It is critical that a data scientist be able to work with unstructured data.

• Data Visualization

Data visualization gives organizations the opportunity to work with data directly.

We hope you have made it to the end of the list feeling confident! If you aren’t, check out Data Science Kickstart by LEAD for a comprehensive online course, designed to get you started in your journey to becoming a data scientist. 

Now that you’re sure you possess all the skills to be able to call yourself a well-informed data scientist, how do you showcase your data science skills effectively and sell yourself?

Interview Tips

Self-presentation

Make sure you’re dressed for the occasion whether that’s picking out a nice suit for your interview or a new shirt with a snazzy tie to match; this is an underrated soft skill. 

Don’t let our Malaysian weather deter you from putting on a blazer. 

Always remember, whether people like to admit it or not, we all judge a book by its cover.

Coming in prepared

One thing that always strikes interviewers is when a candidate brings them a hard copy of their resume.

Sure you might have already emailed them one or sent one over in your application, but it truly is that little bit of effort that makes you stand out.

Many employers have been sent dozens of resumes and find it taxing to have to look for yours through the bunch.

By just handing them yours you make yourself more accessible, meaning that they are more likely to look over yours again.

Another essential aspect, especially for data scientists, is a portfolio.

Bring in a hardcopy even if you’ve already sent it through. It can be seen as a piece of evidence, evidence that you know what you’re talking about and that you didn’t lie on your resume.

Your portfolio is a great way to showcase your skills as well as give your employer a higher chance of looking over your work (especially since they’ll be looking at 20 others).

Answering questions effectively

Always do a trial interview first whether it’s with a mate, mentor, or your mum. Do your research, look up questions interviewers usually ask when hiring data scientists. This is a great resource! 

The thing is you may very well know your stuff, but you may not be able to convey it confidently.

They focus on how they can impress the interviewer, rather than being themselves and answering the question as per normal. That accounts for 80% of this sticky situation.

The Data Science Interview Recipe is a Malaysian friendly recipe that aims to provide you with a guide structure to handle interviews, whether you are attending or conducting one. Get used to the common interview questions and get prepped.

More importantly, this book helps you understand why an interviewer asks you a particular question? What do they want to know?

Once you figure those things out, you will have no problem answering them.

That’s why it has been named a recipe rather than a guide, a handbook, or any other fancy name. It is meant to be a guide for you to follow. Be creative to use it to follow your style. 

Bottom line: make sure you’re confident, not just knowledgeable. It makes a huge impression when you know how to “strut your stuff” per se.

How to give them the right information without them asking

Some interviewers, simply said, don’t know how to interview candidates.

They don’t ask the right questions, nor do they prompt you for more information.

What do you do then? Offer them the right information anyway; you’ve studied the questions, practised your answers, and delivered them well; now just tell them.

For example, the question you were most excited to answer was, “Whether or not a false positive or a false negative is considered a more severe problem?”

But they didn’t ask, you’ve got 10 minutes of the interview left, what now?

“Sir/ ma’am, a topic I find quite intriguing is “Whether or not a false positive or a false negative is considered a more severe problem?”Would it be alright if I shared my thoughts on the topic with you?”

I assure you, next to none of your interviewees will say “no” to that.

It shows initiative and self-starter behaviour which is highly attractive and sought after.

What now?

After all this preparation, it’s only natural to still feel nervous.

After all, this is a position you’ve been waiting for, an interview you’ve been craving, and an opportunity that’s finally arrived.

For an easy to digest and straightforward guide check out our Data Science Interview Recipe.

In this recipe kit, you will have tools that teach you the right way to answer interview questions, including:

• 45-Page Interview Toolkit (PDF)
• 30 Insider’s Interview Questions
• Training Videos
• Access to LEAD’s Pro Group

Get your Data Science Interview Recipe here: https://thelead.io/data-science-interview-recipe

By following our instructions before and during your interview, you will soon find an email attached with an offer letter!