# 10 types of Data Science Interview Questions

https://guttulus.com/wp-content/uploads/2018/12/IMG_0552-1024x634.jpg 1024 634 tony tony https://secure.gravatar.com/avatar/aa9bbdf8f1e6bbf534778ecea7c0c925?s=96&d=mm&r=gRegarded as one of the best jobs of the 21^{st} Century in America, a position as a data scientist promises nothing short of professional growth, financial benefits and rewarding challenges. With more and more companies looking to leverage massive volumes of data to improve their service delivery, data science jobs are in demand and data scientists are the next rock stars.

To become a successful data scientist though, you’ll need to satisfy your potential employers that you truly are cut for the job and there is no better way to do this than in an interview. However impressive your resume might be, if you don’t perform well in the oral interview, your dream of becoming a data scientist, will be quashed very quickly.

Here at Runrex, we have been in the data science field for close to a decade now and in that time, we have made applications to thousands of companies, been subjected to thousands of interview questions and therefore understand the dynamics of data science job interviews.

To help data science job seekers to increase their chances of being considered in interviews, we have put together a list of 10 types of data science interview questions with brief answers which we reckon will be of significant importance to job seekers. Here at the 10 types of data science interview questions;

**10 types of Data Science Interview Questions **

**What is data science? **

Data science is the use of complex and advanced analytical tools and algorithms to process, analyze and draw meaningful insights out of a huge volume of unstructured data. Unstructured data means a set of data generated from different forms and lacks uniformity.

**What is logistic regression? **

Logistic regression or logit model is a means of predicting binary outcome from a linear combination of predictor variables. For example, if you want to predict if a customer will buy a price of a certain shoe or not, the outcome of prediction is binary i.e. 0 or 1. The predictor variables will be things such as the price of the shoe, color of the shoe et cetera.

**Define normal distribution in data science**

Normal data distribution is a form of data distribution where data is distributed around a central value without exhibiting any bias to the left or right hence forming a ‘normal distribution curve’ which is also referred to as a bell curve because of its shape.

**What is linear regression in Data Science? **

Linear regression is a statistical method in which the score of a variable predicted from the score of a second variable. In this case, the first variable is the predictor variable while the second variable is the criterion variable.

**Define power analysis in data science**

Power analysis is an experimental design method which is used in determining and predicting the effects of a given sample size.

**Explain what cross-validation is in Data Science**

Cross validation is a method of validation used to weigh and evaluate how the outcomes of a statistical analysis will generalize to an independent data set. The aim of cross validation is to estimate how accurately a model created will be able to accomplish its designated task in practice.

**What is the Law of Large Numbers?**

The law of large numbers is a theory that explains the result of performing the same experiment over and over again. The theory states that the more times an experiment is conducted, the higher the chances of a model being able to predict future occurrences based on the data acquired from the frequent experiments.

**What is the need for data science in the modern day? **

** Understand the patterns behind the raw data –**The primary role of data science is to understand the hidden patterns behind the raw data collected from various sources. Data science does exploratory analysis of a set of unstructured data to draw insights and patterns from that set of data.

* *** Make predictions–**Data science is needed to help make predictions. Analyzing a set of data helps us to predict future trends in markets and other areas. The ability to predict the future and possibilities is very important and is needed by various sectors.

** Decision making–**The other role of data science, is influencing the decision making in various sectors. By collecting data, analyzing it constantly and drawing conclusions from the set of data, it becomes very easy to make more informed decisions based on the data collected.

**Why did you take up data science as a profession? **

Data science is the future of the world and there is a lot of joy in trying to understand the underlying patterns behind the massive volumes of data. It is also a promising career with rich rewards and poses challenges that help one grow as a professional and as a person.

**Talk to Runrex for more tips on how to pass Data Science interviews **

Want help with getting a job as a data scientist? Want professional advice on how to increase the chances of landing a data science job? Give us a call here at Runrex and we will gladly be of service to you.

We have been down that path and understand what it takes to land a job as a data scientist. Give us a call today and hopefully, we can help you land your first job as a data scientist.

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