Santos Ozoemena is a Data Scientist, author. He has worked and consulted for many startups and big tech companies namely Disney, CVS and T-mobile and so much more. You can subscribe to future updates and projects here - santosbooks.com

Data science interviews, like other technical interviews, require plenty of preparation. There are a number of subjects that need to be covered in order to ensure you are ready for back-to-back questions on statistics, programming, and machine learning. Before we get started, there's one tip I'd like to share. I've noticed that there are several types of data science interviews that companies conduct. Some data science interviews are very product and metric driven.

Decision Tree Algorithm, Explained by Nagesh Singh Chauhan 15 Python Coding Interview Questions You Must Know For Data Science by Nate Rosidi The 6 Python Machine Learning Tools Every Data Scientist Should Know About by Nahla Davies Naïve Bayes Algorithm: Everything You Need to Know by Nagesh Singh Chauhan The Complete Collection of Data Science Books – Part 2 by Abid Ali Awan 21 Cheat Sheets for Data Science Interviews by Nate Rosidi Top Programming Languages and Their Uses by Claire D. Costa The Complete Collection of Data Science Books – Part 1 by Abid Ali Awan 9 Free Harvard Courses to Learn Data Science in 2022 by Natassha Selvaraj DBSCAN Clustering Algorithm in Machine Learning by Nagesh Singh Chauhan

In this blog we gonna discuss about the 10 most asked question from data science, I am assuming that you are a Fresher with 2 years of experience. With More experience questions could change from candidate to candidate. Before starting, I want to clarify one thing, "These set of questions are very common in my experience but can vary company to company". I have a request for you all, please read the questions, try to implement it on your own and then jump to the solutions. This struggles with the questions will make you good at programming, nothing else.

With the dynamically increasing popularity of data science and machine learning, more and more aspirants and enthusiasts are exploring these areas

As we enter a new realm of how we work in a post-pandemic world, you may have noticed that a lot of people are taking new opportunities that may not have been available before. I'm specifically referring to how the advent of remote work has opened up new opportunities for positions where location may have been a barrier before. There's also the unfortunate coincidence that some people may now be seeking new opportunities due to job loss as a cause of the pandemic. Having been through a data science interview myself, I can definitely relate to just how nerve wracking the interview process can be! The data science interview process is generally a multi-phase approach, often consisting of one or more coding assessments, a "culture fit" interview, and of course, a technical question and answer time.

Data science interviews can be cumbersome, and rejections are merely the beginning. While an academic degree, relevant training, skills, and course work are essential to break into data science, it does not guarantee a job or job satisfaction. When it comes to interviews, there are hundreds of reasons for a company to reject a candidate. Of course, it makes more sense for a company to reject a good candidate than to hire a bad one. But, a talented data science professional stands above all, making sure to stay ahead of the curve.

Data science interviews, like other technical interviews, require plenty of preparation. There are a number of subjects that need to be covered in order to ensure you are ready for back-to-back questions on statistics, programming and machine learning. Before we get started, there's one tip I'd like to share. I've noticed that there are several types of data science interviews that companies conduct. Some data science interviews are very product and metric driven.

This is Part 2 of the Interview Question series that I recently started. In Part 1, we talked about another important data science interview question pertaining to scaling your ML model. Be sure to check that out! A typical open-ended question that often comes up during interviews (both first and second round) is related to your personal (or side) projects. And trust me when I say this, this question is the best thing that can happen to you during an interview.

I have been on both sides of the data science interview process; therefore, I know how stressful it is for the applicant, but also how important it is for the company to find the right candidate. Over the years, I have observed that anticipating audience needs is the most important factor at each interview stage; yet data science candidates often over-index on technical acumen, and neglect the fact that every evaluator is reviewing different attributes. Worse than that, data science candidates tend to go down rabbit holes such as Bayesian parameter estimation. Going this deep into a highly technical niche subject has two potential risks: Having an interviewer's eyes glaze over because they are not the right audience, or worse, the interviewer has a deeper technical understanding about that particular subject and will trip you up in your answer! In my 11 years building a data science career, I have found there to be four main stages to an interview process, each with distinct audiences: the initial phone screen, the technical evaluation, the "take home" assignment, and a behavioral assessment.