Top 10 Analytics Interview Questions and Answers

Approximately 80% (or even more) of job interviews start with a question – Tell me something about yourself/ Please walk us through the CV. But what happens after that in an Analytics Interview? Most recruiters are looking for strong basics and a clear understanding of simple Analytics fundamentals. We have tried to pen down a few questions that you may expect during your upcoming interview –

What is Logistic Regression?

Ans. A technique to predict the binary outcome from a linear combination of predictor variables.

How to determine the data type of an object in R?

Ans. Class () is used to determine the data type of an object.

What is the difference between Informat and Format?

Ans. Informat is used to read the data while Format is used to write the data. In other words, Informat tells SAS to read the number in a particular format while Format tells SAS how to print the variables.

What is the difference between supervised and unsupervised learning?

Ans. Supervised Machine Learning is the one where a machine learning task is trained for every input with a corresponding target or response variable. If the target is expressed in some classes then it is a classification problem and if the target is continuous it is regression.

Unsupervised Machine Learning is the one where the Machine Learning task is trained only with a set of inputs with no target variable. An example of such learning is Clustering.

Which function is used to build linear regression model in R?

Ans. The lm () function is used for fitting a linear regression model.

How to include and exclude specific variables in a data set?

Ans. The KEEP and DROP statements include and exclude specific variables. The DROP statement excludes the specified variable from the data set while the KEEP statement retains the specified variables in the data set.

What is p-value?

Ans.  P-value is the probability value that the data would be at least as extreme as those observed under the presumption that the null hypothesis is true. In other words, the probability of failing to reject the null hypothesis.

Why is normal distribution important in inferential statistics?

Ans. The importance of normal distribution in inferential statistics runs behind the Central Limit Theorem which states that as sample size increases the sampling distribution of sample means follows a normal distribution.

How to check autocorrelation assumption in linear regression in R?

Ans. durbinWatsonTest() function

Which are the popular R packages for decision tree?

Ans. rpart, party

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