Being called the ‘Sexiest job of the 21st century’ by Harvard Business Review, data
science is the next big thing and data scientists are rockstars. As per research by IBM, it predicts the demand for data scientists will soar by 28%, by 2020.

Why is this growing so much?

Because employers use data science to derive insights from raw data to solve most of their problems. Solutions originating from data analysis are more logical and accurate. For this kind of an interview, the interviewer will ask a wide range of questions which will test your technical knowledge and your communication skills too. Here’s a list of frequently asked data science interview questions (with answers) which will help you prepare for, and ace the data science interview.

## What is linear regression?

Statistics is the backbone of data science. It’s only using methods of statistics that the data scientists analyse raw data and create predictions and solutions. You need to have advanced knowledge of statistics for being a successful data scientist. The interviewer will test your fundamentals of statistics for sure. As far as the answer goes, in applications of data science the scientists are very much interested in the relationship between two or more variables. Linear regressionis a powerful statistical method that is commonly used to quantify and correlate that particular relationship, hence it also commonly used in research. Other questions about sampling methods, Type I and Type II errors, P value, R squared value etc., maybe be asked.

## What’s your favourite data visualisation technique?

This falls under data modelling. This is what is of ultimate value to organisations.
Converting data into actionable results is difficult, so use data models to describe your past experiences: what techniques were used, what were the challenges faced, whether the project was successful. The question above is used to test your education and knowledge you’ve had formally about data modelling techniques. If you fail to describe any technique’s theory and assumptions, it won’t have a great impression on the recruiter. Some other questions that can be asked are about logistic regression models, time series model, representing data in 5D, K means clustering, L1 and L2 regularisations etc.