If you want to have a career in data science, knowing Python is a must. Python is the most popular programming language in data science, especially when it comes to machine learning and artificial intelligence. To help you in your data science career, I've prepared the main Python concepts tested in the data science interview. Later on, I will discuss two main interview question types that cover those concepts you're required to know as a data scientist. I'll also show you several example questions and give you solutions to push you in the right direction.
Preparing for an interview is not easy – naturally there is a large amount of uncertainty regarding the data science interview questions you will be asked. No matter how much work experience or technical skill you have, an interviewer can throw you off with a set of questions that you didn't expect. For a data science interview, an interviewer will ask questions spanning a wide range of topics, requiring strong technical knowledge and communication skills from the part of the interviewee. Your statistics, programming, and data modeling skills will be put to the test through a variety of questions and question styles – intentionally designed to keep you on your feet and force you to demonstrate how you operate under pressure. Preparation is a major key to success when in pursuit of a career in data science.
This article takes you through some of the machine learning interview questions and answers, that you're likely to encounter on your way to achieving your dream job. But it doesn't have to be this way. These questions are collected after consulting with Machine Learning Certification Training Experts. We'd ask the following types/examples of questions, not all of which are considered pass/fail, but do give us a reasonable comprehensive picture of the candidate's depth in this area. During her career she has interviewed over a 100 candidates.
Are you aspiring to become a data scientist, but struggling to crack the interviews? Getting a break in the data science field can be difficult. Doubly so, if you're coming from a non-data science background (which in all likelihood you are). The stories you hear from other aspiring data scientists can make interviews feel more intimidating and daunting. So you better be prepared before facing the interviews. What kind of questions can be asked? How can you prepare and what are the resources you should refer to? What is the structure of a typical data science interview? How should your body language be? These are just some of the questions you'll have in mind.