Important Q &A Examples for Data Science Interview

Important Q &A Examples for Data Science Interview

According to an article in Harvard business review, ‘data scientist is the sexiest job in the 21st century’. And according to sources, the demand in this sector will increase by 28% in the year 2020. Data scientist is currently one of the most popular job titles in the world.

Although job opportunities are a lot in this field but grabbing a job is difficult because a data scientist has to have a lot of skills. Thus, it is very essential that you make a profound impression in the job interviews.

So, here are some examples of Q&A for a typical data science interview which will be beneficial to all of you who are passionate about this subject and want to become a data scientist.

Some Important Q &A Examples for A Data Science Interview

1. Mention Some Differences Between Supervised and Unsupervised Learning.

Supervised learning uses known data as input while in unsupervised learning we use unlabeled or unknown data. The feedback mechanism is available in supervised learning while the same is not present in the case of unsupervised learning. Some of the most common supervised learning algorithms are decision trees, logistic regression and support vector machine. Whereas some of the most common unsupervised learning algorithms are k means, clustering, and hierarchical clustering.

2. How Can You Implement A Decision Tree?

For implementing a decision tree, you will need an entire data set as input. Then a calculation of the entropy for target variables as well as predictor attributes has to be done. Then the information gain form all the attributes are to be found out. The attribute having the highest information gain is considered to be the root node and the same procedure is followed for every branch until and unless you get the decision node of each of the branches.

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3. How Can You Build A Random Forest Model?

A random forest comprises of a number of decision trees. To build a random forest model, you need to consider K features from a total of say M features. Here the K features will be a subset of M features. Using the best split point, node D is calculated from the K features. The node is again divided into daughter nodes using the best split. The leaf nodes are decided after repeating the above steps two or three times.

All the above steps need to be repeated as many times you want to build trees in the model. And finally, with the accumulation of these trees, you will have your random forest model.

4. How to Avoid Overfitting of Your Model?

There are three main steps to avoid the overfitting of any model. They are given below:

  1. Keep the model very simple by taking very few variables.
  2. Use cross-validation techniques like k folds cross-validation
  3. Use regularization techniques like LASSO which will handle parameters that cause overfitting generally.

5. What Are the Benefits of Dimensionality Reduction?

Dimensionality reduction actually helps in compressing data and it also reduces storage space. Also, it is very beneficially for proper interpretation and data processing.


Becoming a data scientist can be a bit challenging but I hope that this article has given you a fair idea of what to expect in data science interviews. The above 5 questions should be taken as an example to start practice. So, prepare well and all the best for your upcoming interview sessions!

Last updated:1/31/2020 1:37:21 AM
Tyrion Grejoy

Tyrion Grejoy

With more than 5 years of teaching experience Tyrion is one among the finest professor of Statistic and data science.


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