Data Science Interview Question & Answers

Off late, the field of data science has attracted a lot of professionals owing to the high salary it offers and also the vast opportunity. If you have been planning to pursue a career in data science then it's time you prepare yourself well to face the questions you would be asked during your job interview. Here is a list of popular data science interview questions and answers.




What is data science?


Data science is the study of extracting structured and unstructured data by putting to use programming skills, various tools, learning principles and knowledge of mathematics and statistics. Data science can help a business gain a competitive edge by helping them get user insights, develop better customer service, reducing costs, development of new products, etc.


How a deployed model can be maintained?


You can maintain a deployed model by:

  • Monitoring it to get a clear understanding of its performance

  • Evaluating it to understand if you need introduction to new algorithms

  • Comparing it to determine the best performing models

  • Rebuilding the best performing model (based on existing data set)


What is a recommender system?


It is an information filtering technique that helps you predict the preference or ratings that users are likely to give to a product.


What is bias?


Owing to oversimplification of machine learning algorithms, an error that is likely to occur in the system is termed as bias.


What is the need of conducting A/B testing?


AB testing is an experiment in which two or more variants of a page are shown to a user and statistical information is analyzed to gauge the variations that are performing better and can lead to a high conversion rate.


What are the steps you need to undertake for a data analytics project?


Few important steps that you need to take before starting a data analytics project include:

  • Get an understanding of the businesses existing problem

  • Study the data

  • Start with modeling the data by finding values and transforming variables

  • Start running the model to analyze the data result

  • Validate the model with new data set

  • Implement the new model and track the results


What do you mean by ensemble learning?


Ensemble learning can be defined as the art of combining various individual models to improve on the overall predictive power and stability of the model.


What is deep learning?


It is a sub-field of machine learning in which data goes through multiple numbers of non-linear transformations to get an output. Deep learning is also known as deep neutral network as it uses multi-layered artificial neural network network.


When would you update an algorithm in data science?


When the underlying data source is changing or when the data models have to be evolved as data streams using infrastructure.


What is P-value?


P-value denotes the strength of the results of hypothesis tests in statistics; it ranges from

0 to 1.


What is the difference between supervised and unsupervised learning?


Under supervised learning:

  • Input data is labeled

  • Used for prediction and as a data training set Enables classification and regression


Under unsupervised learning:

  • Input data is not labeled

  • Used as a input data set for analysis

  • Enables classification, density estimation and dimension reduction


Which are various types of deep learning frameworks?

  • Caffe

  • Chainer

  • Keras

  • Pytorch


‘People who purchased this also bought’ recommendation is seen on e-commerce websites as a result of which algorithm?


This is the result of a recommendation engine that predicts the interest of a user based on preference of other users. For instance, the sales page shows that people are purchasing tempered glass or insurance along with a phone. So the next time someone buys a phone, another user can see glass and insurance as recommendations.



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