Becoming a data scientist is considered a prestigious trait. Back in 2012, Harvard Business Review called'data scientist' the sexiest job of the 21st century, and the growing trend of roles in the industry seems to be confirming that statement. To confirm this sexiness is still ongoing, the info from Glassdoor shows being a data scientist is the second-best job in America in 2021. To get such a prestigious job, you have to go through rigorous job interviews. Data science questions asked can be very broad and complex. This is expected, considering the role of a data scientist usually incorporates so many areas.
To be honest with you, getting your first job is never easy. It is, in fact, one of the hardest things in your life and you need to put your best effort to find a job in your dream company. The single most important reason for failing those coding job interviews is the lack of knowledge and practice. If you don't don't know much about what to learn then you are bound to fail, hence it becomes increasingly important that you prepare hard in advance. Unfortunately, I learned this a little too late, after spoiling my chances at Microsoft and Amazon, but you don't need to.
Data science is an attractive field. It's lucrative, you get opportunities to work on interesting projects, and you're always learning new things. Hence, breaking into the world of data science is extremely competitive. One of the best ways to start your data science career is through a data science internship. In this article, we'll look at the general level of knowledge that's required, the components of a typical interview process, and some example interview questions.
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.