Studying and building a career in data science can be difficult. Especially when you are just starting your journey. Are you looking to build a career in data science? Got yourself a big interview? Let us help you prepare for your big day.

Here are some important data science interview questions to help you get through the interview:-

Question 1: What is data science?

Answer: In simple words, data science is the study of large chunks of cumulative data. In other words, the method of recording, storing and analyzing data to extract useful information to facilitate decision making is known as data science.

Question 2: What is the major difference between long and wide format data?

Answer: In wide-format data, a subject’s repeated responses are recorded in a single row and each response is in a separate column whereas, in long format data, each row represents a subject.

Question 3: What do you understand by Normal Distribution?

Answer: When the data is not distributed around a central value with any bias to form a bell-shaped curve is known as a normal distribution of data. Random variables are always distributed in the form of an asymmetrical bell-shaped curve.

Question 4: Why does data cleaning play a vital role in the analysis?

Answer: Data cleaning helps in cleaning data from multiple sources to help transform it into a format that data scientists can work with and results in increased accuracy of the model.

Question 5: What do you understand by Cluster Sampling?

Answer: It is a technique to simplify the study of target population across a wide area and simple random sampling cannot be applied. It is a probability sample where each sampling unit is a collection of elements.

Question 6:  What do you understand by Systematic Sampling?

Answer: It is a statistical technique where elements are systematically selected from a sampling frame. The list is progressed circularly so once you are through with the complete list, it samples from the top again.

Question 7: What do you mean by cross-validation?

Answer: Cross-validation is a validation technique used to evaluate how the outcomes of a statistical analysis will generalize to an Independent dataset in a given model. The goal of cross-validation is to test the model in the training phase.

Question 8: What do you understand by Machine Learning?

Answer: The study and construction of algorithms used to make predictions on a given set of data are called Machine Learning.

Question 9: What is Supervised Learning?

Answer: It is a machine learning task that involves inferring a function from labelled training data consisting of a set of training examples.

Question 10: What is Unsupervised learning?

Answer: It is the type of machine learning algorithm used to draw inferences from data sets. These data sets consist of input data without labelled responses.

Question 11: What is a Linear Regression?

Answer: It is a statistical technique where the score of variable Y “the criterion variable” is predicted from the score of variable X “the predictor variable”.

Question 12: What is Collaborative filtering?

Answer: The process of filtering to identify patterns or information by collaborating viewpoints, various data sources and multiple agents.

Question 13: How do you describe the structure of Artificial Neural Networks?

Answer: Artificial Neural Networks consists of inputs which get processed with weighted sums and Bias, with the help of Activation Functions.

Question 14: Explain the term “Gradient descent”.

Answer: Gradient Descent is a minimization algorithm that minimizes a given function or the activation function.

Question 15:  What is the role of the Activation Function?

Answer: It is used to introduce non-linearity into the neural network to help it learn more complex functions.

Got More Questions?

For comprehensive data science interview questions, you can take guidance from a data science course online. This course will help you gain expertise in machine learning algorithms and understand the basic concepts of Statistics, Time Series, and introduction to Deep Learning.