Ace The Toughest Data Science Interview With These Stunning Tricks
A Guide for data science interview preparation.
Today, Data Science Interview preparation is one of the biggest deals for everyone. Almost everyone finds an interview difficult, but a data science interview can be much more difficult than the rest and sometimes even tricky. Every interview is a learning process, whether you pass it or not. A data science interview is challenging as it consists of a few tricky questions. Data science holds many roles and responsibilities, and the questions might differ little according to the data science job role. I.e. If you have applied for a data scientist position, you know that the recruiter can ask for coding and algorithm elements. They are the basic questions for which candidates must be prepared during the data science interview. But the core questions differ for various other data science job roles.
Negotiating salaries is not easy for many, but overcoming these challenges after hard work can add a few extra savings to your pocket, with our positive desire for you to achieve the best salary by preparing for the data science interview. In this blog, I would like to share some insights on what you need to prepare for a data science interview.
What Should You Know During Data Science Interview Preparation?
Go Through All Your Coding Skills
Data science is a technical field, and coding interviews are among the main rounds in a data science interview process as a data scientist's role is to collect and analyze data into useful data. So a coding test is not just to see your technical skills but to know your thought process and approaches to how you divide the complicated questions into simple solutions. Hence, to ace a data science interview, you need to prepare yourself with coding thoroughly.
These coding questions also help determine your logical reasoning to solve real-world problems. It is valid that there are various solutions to a single problem. But the real problem is to find an optimized solution in addition to run-time and storage. So you must come up with the best solution.
The recruiter even looks at your overall code quality by checking if you have considered all crucial cases in your solutions or not.
Because you now understand the significance of the coding problems, you must prepare yourself to answer them correctly in the time allotted. You should practice as many data science interview questions as possible to obtain a deeper understanding of various circumstances. Concentrate more on real-world issues.
By logically coming up with an effective solution, you will be able to break down difficult questions into simple elements.
Don't be put off by the types of inquiries that may appear intimidating at first. You'll need some time to prepare them, but you'll need a strong understanding of basic programming ideas and Machine Learning techniques. You can also develop several solutions to a single problem, analyze their strengths and limitations, and choose the best feasible way to gain a more comprehensive understanding. Taking part in different hackathons events helps you hone your logical coding skills.
That is why you must have a clear grasp of the product that must be produced to synchronize your efforts and truly apply them to the product.
Spare more in analytic and logical thinking
If you have an issue, you must be able to transform it into a problem that data science can solve. So the interviewers are looking to see whether you can take the context from the business side and turn it into an issue that can be solved using data science.
Hone your product/service sense
Your comprehension of the product/ service (domain-specific) is referred to as product/service sense. It is more important to clearly understand the context than to solve problems and become bogged down in technical minutiae. You must understand the goal of the product or service you're creating, why it's essential to you, and how you'll use it to help others.
Brush up your communication skills
You should be good at communicating your thought process and understand your partner's problem who you are working with. As data scientists have to work with other teams, they must have good communication skills. They even have to communicate in a way that everyone understands the results or what is the next objective of the organization.
You don't have to know what the problem is to be able to solve it. It indicates that you should understand how to apply data science to the situation. As a result, you must be able to devise a framework or an effective technique to solve the problem and provide a superior product.
How to deal with Machine Learning, statistics, and modeling questions?
Generally, these are non-coding questions. The recruiter is testing you on both technical knowledge and theory and implementation. So, the questions from the recruiter usually are
Concentrate on theory and implement it
Do you know how to improve the theory and implementation knowledge? I suggest that you should have a few personal project stories where you can talk in detail about the data science projects you have completed in the past. So, you have to be able to answer questions like:
What assumptions do you need to validate to utilize the model correctly?
Why did you choose a specific model?
Trade-offs of that specific model
If you can answer these questions, you can prove to the recruiter that you know both theory and how to implement it practically in a project. These projects can be academic, personal projects, or any other project that you would have recently worked upon. These are some of the modeling techniques you need to know:
K- nearest neighbor
Gradient boosting and many more
These are the few common models that usually data scientists should know and have experience with how to implement. Talking about your project is the best way to portray your talent so that interviewers would know that you are experienced in projects. Suppose you want your interview to be more effective than showing how to implement the model. Show how you categorize clean data and create a data pipeline for it. See the results and communicate the same with other stakeholders. So, if you show all your data science process from start to end, i.e., obtaining data, delivering results to the stakeholders, and explaining all your process in detail. This would impress recruiters, and it would also state that you are a data scientist.
Questions in these tests can help recruiters understand how you respond to different work situations. How good you are at solving problems and achieving successful outcomes? The recruiters throw some questions of conflict to you and then see how you resolve it. The importance of these questions is for HR to know whether you are the right fit or not.
These are some of the behavioral questions that are typically asked during a data science interview:
Give an example of team conflict
How have you utilized data to draw an opinion or conclusion?
How have you ever had a misconception in a data science project?
Have you ever used data to improve customer experience?
Explain a scenario of how you worked in your team?
The data science behavioral questions are divided into parts.
Select stories to refine
You have to recall your experience and recollect 4-6 stories that can demonstrate both the conflict and the resolution. You must have your own story so you can answer behavioral questions. If you would explain these stories in a hypothetical situation like you would have done if this happened, then it would not impact the recruiter. So this would make them feel that you do not have experience because you have not explained a real story of your work to the questions asked.
Divide the story in the STAR framework
Start with the situation so the recruiter may know the basics of the storyline.
T - Task
Explain your roles and responsibilities
A - Action
Let them know what actions you took and did not take.
R - results
And this is the most important thing. Let the recruiters know what actions you took and how everyone was a beneficiary of the action that you took.
General data science interview preparation tips
What are the best ways to prepare for a data science interview? One of the key obstacles is that there are many problems all over the internet, and you must prepare for your data science interview in an orderly and organized manner.
How to prepare for a long-term data science interview that will take place in two to three months and a short-term interview that will take place within 4 to 5 days?
Long-term data science interview preparation
You must separate the questions, such as pre-questions and post-questions, and some videos and content to study in between. Then attempt the pre-section and see how you do and your shortcomings, and make some notes. The goal is to note where you're weak, fast, or slow so you can figure out which parts you need to work on more.
I suggest you break-down questions like:
Data science questions
Machine Learning models
How To Prepare for a short-term data science interview?
I wouldn't recommend studying for a short-term interview because you need to unwind the night before. Get a good night's sleep and a good breakfast the next day. You must be at your top strength, and if you worked out hard the day before, you would most certainly be depleted and weary when giving an interview. So relax and trust yourself because that's how you'll perform at your best.
Data Science is one of the toughest interviews, but that does not mean you cannot crack it. It just requires dedication and skills with knowledge, and you can do it. Just read this blog pre-prepare yourself to ace the data science interview. Our courses at Learnbay help candidates in resume building and portfolio creation. We assure job referrals and offer a course in data science with a job guarantee or you can claim your fees back. But remember, certificates and projects are necessary, but it is even more necessary to communicate what you know freely.