Interviewing for data science jobs can be very intimidating at times because you are not just being interviewed for a company; you are being interviewed for a culture, a team, and possibly a good fit for your skills.
Data science interview questions are technical, involving a diverse base of knowledge from statistics, machine learning, programming, and more to solve problems on the fly.
They can take many forms, such as parsing data with R and Python, simulating a model with Google Sheets, or pivot tables in Microsoft Excel.
An interview is your chance to show a company your knowledge and passion for data science. It is a chance for you to demonstrate that you are a good fit for their team and make the case that they should hire you.
Data scientists are incredibly sought-after in today’s market, and it is easy to see why. The amount of data that we produce continues to grow every year, and data scientists are the ones who can take that data and turn it into something useful for companies.
So if you are gearing up for your upcoming interview, here are a few tips to help you prepare:
1. Do Your Research About The Company
This is step number one because it is so important, and yet, it is easy to overlook. You must research the company before your interview and keep a few things in mind as you do:
What do they do? What products or services do they offer?
What is their reputation in their industry and beyond? Is it all positive? Any controversies or negative press?
Who are their customers, clients, or partners?
Who are their competitors? What is different about their approach compared to those of competitors?
What does the company care about in general? Does it seem like there is an emphasis on sustainability, community involvement, profit margins, or something else entirely?
How long has the company been around, and where are they located (if not online)? Have they recently gone through any major changes, either internal or external?
2. Practice Practice, Practice!
The best way to practice for an interview is to do a mock interview with someone who works in the industry and can give you feedback on your answers.
3. Show interest in their current projects and successes!
Candidates must show that they are interested in what their potential employer has been working on recently or have had success with past projects because it shows them how excited they are about working there!
4) Be prepared with questions!
It is a good idea for someone to come prepared for an interview by having some questions ready.
This way, there will be less awkward silence after an interviewer answers one of yours and more conversation about what exactly interests them most about data science as well as
5. Know Your Resume
Make sure that everything on your resume is accurate and up to date.
If there is any confusion about a role or skill listed on your resume, this is the first thing that you will need to clear up during the interview so make sure there are not any surprises in store for the interviewer.
6. Understand The Types Of Questions And Formats You Will See In An Interview.
Before approaching an interview, it is essential to understand the types of questions you will be asked and the types of responses they are looking for.
Make sure you know how to respond to specific questions, like “Tell us about your most difficult project,” or scenario-based questions, where you solve a problem.
7. Know Common Data Science Terminology And Buzzwords.
You should be conversant with the terms used in the field so that when someone says them, you understand what they mean and can answer appropriately.
Knowing these terms will also help when it comes time for you to ask questions during the interview process and when discussing your portfolio with potential clients or employers.
Some popular data science terms include Big data, machine learning, Deep Learning, Bayes Theorem, Behavioural analytics. E.tc.
8. Brush Up On Your Math
The foundation of data science is math. To be effective when undertaking data analysis or creating machine learning models, you must have a solid understanding of the fundamentals of statistics, linear algebra, and other essential mathematical topics.
Acing the math questions during your interview will send a clear signal that you have what it takes to succeed in the field, so make sure you practice as much as possible before going in.
9. Practice Coding On A Whiteboard
One of the most common ways for companies to evaluate candidates is through coding challenges on a whiteboard.
This is not anything like coding in your favourite text editor, but it is an essential component of the interview process nonetheless.
Make sure you take some time to practice coding with a pen and paper. It can be an entirely different ball game than typing on your keyboard!
10 Practice Interviewing
Do mock interviews with friends or family members who can give you constructive criticism on body language and verbal responses.
Tell your friends to interview you, and make sure you are looking at them (not at your phone or laptop) when they are talking, smiling, and answering questions honestly but confidently.
11. Learn Statistical Concepts And Common Algorithms.
You do not need to memorize every algorithm out there, but it is a good idea to understand the most common ones used in data science: linear regression, logistic regression, random forests, Naive Bayes classifiers, decision trees/random forests/boosting/bagging, support vector machines (SVM), k-means clustering, and principal component analysis (PCA).
It is also good to be able to discuss why these methods are used when they are. For instance, PCA is often used to reduce the dimensionality of a dataset before fitting a model.
I hope this has been helpful to you in deciding what to do before and during your next Data Science job interview.
Do not be afraid to take a few extra moments to prepare, even if it is your dream job.
After all, the right job opportunities do not come along often, so go ahead and use this time to make sure you are putting your best foot forward. Good luck!