Top 51 Data Science Interview Questions!

Data Science is one of the most dynamic fields in technology attracting innumerable candidates towards it. However, not everyone ends up landing on a good Data Scientist profile. With the cut-throat competition among the candidates, you need to have the edge to have an upper hand. Therefore, it is very much important for the aspirants to know those common and tricky questions that are asked by the interviews.  

Before going through the Interview question, it is suggested that you get you acquire the fundamental knowledge of Data Science. Learn this blog What is Data Science | Start Your Career in Data Science Today! and get a strong foothold on the concepts of Data Science!

In this Blog, following you will learn about the top Data Science questions that are asked in the interviews. Here the questions are divided in following segments:

• Common Questions Asked in Data Science
• Machine Learning Questions
• Programming Questions
• Data Visualization Questions

Common Questions

Any Data Science interview started with some basic questions that set the tone for the rest of the process. These questions are short and direct, usually not tricky.

 These questions can be from any related subject, and having just the right answers to these questions give your interviewer the idea about your fundamental knowledge.

This section will highlight the top Common Questions that are asked by the interviewers during a Data Science interview!

Ques 1.  Differentiate between Data Science, Machine Learning, and AI.

Ans: Data Science, Machine Learning, and Artificial Intelligence, are inter-related fields, but are often mistakenly used interchangeably. Following table will clear the doubts in a better way:

Data Science	Machine Learning (ML)	Artificial Intelligence (AI)
Implementation of technology, computations and business skills to make business decisions.	Practical implementation of AI.	Equipping machines with knowledge and decision-making ability.
A subset of AI.	A subset of AI.	A bigger set.
Includes slicing and dicing the complex and large datasets to make inferences.	Includes building programs that build cognitive intelligence in machines.	Includes intelligent algorithms.
Applied in Target advertising, Internet search, Augmented Reality, etc.	Applied in Self-driving cars, financial services, etc.	Applied in Chatbots, voice recognition systems, data refining, etc.

Ques 2.  What do you mean by Data Integrity?

Ans: Data Integrity is term used to denote the standards made and applied in the Database Management Systems, to ensure data consistency and data correctness.

For example, if someone enters ‘Name’ in the place of the ‘Email Address’, then the Data Integrity constraints will be enforced and the form will not accept the wrong data for any entry.  

Data Integrity practically ensures the insertion of data, updating the data, or any other operations are carried out in the right manner and do not affect the quality and consistency of the data. Data Integrity also ensures that the data is safeguarded from any outside factor.

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