Top Data Science Science Interview Questions & Answers

StarAgilecalenderJune 27, 2022book15 minseyes2023

Data science is emerging as one of the most promising fields for students, offering some incredible job opportunities for many job-seekers who wish to be placed in the industry. However, hiring such candidates can expose the abundant interest of eligible candidates in this field, which is why you need to prepare thoroughly for an interview after achieving data science certification.


Here are some basic questions to help you get started with your preparation for an interview in data science. These questions cover some of the introductory topics in data science and are relevant to the understanding of any student looking to crack a job.

Data Science Interview Questions & Answers

1. What is the meaning of data science?

A: Data science is a multi-discipline that includes several tools and techniques that can be applied to gather patterns and insights from raw data by using several types of analysis, which are collectively known as data science. It begins with an understanding of the business, goes through data mining and data exploration, and ends with data visualization.

To sum up, data science is a field of study that encompasses the expertise of the domain through a conglomeration of programming skills and combined knowledge of mathematics and statistics. The study aims to deliver meaningful insights from the pool of available data and draw reasonable conclusions. 

2. How do data science and data analytics stand apart?

A: Here are some basic points that separate the Data Science and Data Analytics:

While data science involves transforming data using technical analysis, data analytics deals, in large part, with effective decision-making for the business. Data science focuses on driving innovation by addressing problems and building connections. On the other hand, data analytics is the opposite of predictive modelling and focuses on extracting present context from historical data.

While data science makes significant use of various mathematical and scientific tools, data analytics uses fewer tools to deal with the problems in a specific field. While the two are quite different, they find common grounds in the skills that overlap in terms of work performed at the two.

3. Elucidate the concept of selection bias and its various types.

A: Selection bias usually occurs when there is no random selection of items from a population. When a researcher decides which population items will be studied, selection bias can occur. It is a type of error and results in distortion of statistical analysis. There are several types of selection bias:

  • Sampling bias: It is a type of error that results from a non-random sample in a population which reduces the chances of other items being included in the sampling process.
  • Data: When a specific data subset may be selected for implicating a particular conclusion or rejection of bad data.
  • Attrition: This results from losing participating items or discounting trial subjects who could not run-up to the completion stage.

4. What are feature vectors?

A: In data science, a feature vector is an n-dimensional vector that represents the numerical features of an object. The mathematical depiction of the vectors makes it easier to analyze.

5. Explain the steps in making a decision tree.

A: The following steps can be followed to prepare a decision tree:

  • The complete set of data is taken as an input.
  • Conduct a test to identify a split that will help maximize the separation of classes into two separate data sets.
  • The input data is divided by the application of a split.
  • Steps 1 and 2 are applied once again to the separated data.
  • As soon as a criterion is identified, the process is stopped.
  • Complete the pruning process by avoiding too many splits

6. Define root cause analysis.

A: The concept of root cause analysis explains the problem-solving techniques used to eliminate or isolate the root causes, which usually result in a fault or a problem. Usually, a factor can be counted as a root cause if its removal from the problem fault sequence results in the aversion to an undesirable event.

7. What is the meaning of cross-validation?

A: It is a type of model validation technique that helps evaluate how the outcomes of statistical analysis will apply to an independent data set. Cross-validation is mainly employed in a background where the main target is to forecast and estimate the accuracy with which the model is likely to perform. Cross-validation helps limit several problems such as overfitting and helps gain insights into the model.

8. What is the purpose of performing A/B testing?

A: Students usually learn in-depth about A/B testing during a data science course. It is a type of statistical hypothesis, and as a testing module, it is used to assess random experiments with the help of two variables, namely, A and B. The target of A/B testing is to detect any changes to a web page that may be able to help maximize or increase the outcome of a given strategy.

9. Explain how frequently should an algorithm change?

A: Usually, an algorithm should be upgraded under the following circumstances:

  • There is a need to evolve a model with the regular streaming of data through a given infrastructure.
  • There is a change in the underlying data source.
  • A case of non-stationarity has arisen.

10. Mention the reasons why resampling may be done.

A: Resampling may be done in any of the following circumstances:

  • When there is a need to estimate the accuracy of a given sample, statistics using the subsets of accessible data
  • When there is a need to draw random replacements from a given set of data points
  • For substituting labels on data points when any significant test is being performed
  • To validate models using any random subset such as cross-validation or bootstrapping.


With the backing of these questions and answers, you will be able to make a breakthrough impression on your interviewers. These basic questions will certainly help you get started preparing to ace your interview. Once you prepare them, you can certainly advance towards preparing some of the more technical questions for this purpose. Also, if you are really serious about your data science career then enrolling in our Data Science Certification would be the best career decision you’ll ever make. You can land your dream job with our job guarantee program that we have made in collaboration with IBM with 300+ Hours of Practical Assignments, 6 Months Experience Certificate, and Dedicated mentors available at no-cost EMI. 

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