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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Introduction to Big Data

If you were asked to select the most profound technologies that have impacted human existence and continue to do so, any such selection would be incomplete without a mention of Big Data.

Big Data is big not only in terms the data output it throws up, which is so large that traditional computer systems cannot handle it; but also in terms of the sheer magnitude of the ways by which this data can be put to use.

Big Data assumes extreme significance in the context of the tech world we live in today, where there are mountain loads of data from possibly every source. Every object has the potential to throw up data, which could consist of three V’s-variety, velocity and volumes.

These data in themselves may not be of much value unless they are harnessed. Once this is done, it becomes meaningful enough to make sense and enable decision-making. This is exactly what Big Data enables. This in a nutshell is what the whole idea of Big Data is.

When did Big Data begin?

Big Data has a history that in a sense stretches to the 1960’s and 1970’s, when it could be said to have begun in its infantile stages. However, it was much later, only in about the middle of the 2000’s, that Big Data came to be structured as a subject with the huge loads of data that social media companies such as Facebook or online services such as YouTube threw up.

This information can be either of these:

-        Structured, meaning they are already in a state of organized form, which enables deployment for analysis straightaway;

-        Semi-structured, which means that they are not fully formed into a database, but can be processed further;

-        Unstructured data, which consists of data that is not traditionally classifiable as being organized. It is in this area that Big Data has the most efficient role to play, as it helps organizations to make sense of this kind of even highly unstructured data, which could contain valuable insights when tapped into rightly.

Challenges in Big Data

Big Data is being hailed as one of the technologies that can transform our way of life for the future generations, because it has uses in unimaginably wide arenas of activities. Banking, education, infrastructure, governance, and law and order are only a handful of areas that can undergo complete change with the adaption of Big Data.

Yet, this said, there are a few formidable challenges in adapting Big Data. Let us examine a couple of these:

Data is increasing multifold, which means that even a technology that is as resilient and robust as Big Data has to constantly be on its toes in upgrading its capabilities. This is because as of now, data volumes have a capacity to double every couple of years, and this is a huge scale of multiplication. Big Data systems have to be constantly increased and more importantly, made more efficient in collecting data and then making use of it.

This means that a lot of resources are required to clean and sift data that gets generated at such a humongous rate. A good part of any organization’s resources get consumed on this activity, which although inevitable, is a drain on resources. Organizations have to discover newer technologies that make this happen more efficiently.

Please feel free to let us know what you think of this blog! Interested in making a career in Big Data? Then, take a look at Simpliv’s courses, which will help you take your next steps in this profession.