The Common Data Science Interview Questions To Remember..!!

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Data science interviews are often considered to be difficult and it might be difficult for you to anticipate what questions you will be asked. The interviewer can ask technical questions or throw you off guard with questions you hadn’t prepared for. To pursue a full-fledged data science career, it is important for you to be up to date on an array of questions that might be asked during the interview, ranging from programming skills to statistical knowledge, or even field expertise and plain communication skills.
Here is a segmentation of the various categories along with the list down of the possible questions you can expect in each category as an interviewee during a data science interview.
As an interviewee, it is essential for you to be prepared on statistical questions since statistics is considered to be the backbone of data science.

  • What are the various sampling methods that you know of?
  • Explain the importance of the Central Limit Theorem.
  • Explain the term linear regression.
  • How is the term P-value different from R-Squared value?
  • What are the various assumptions you need to come up with for linear regression?
  • Define the term- statistical interaction.
  • Explain the Binomial Probability Formula.
  • If you were to work on a non-Gaussian distribution, what is the dataset you would use?
  • How does selection bias work?

Interviewers may ask completely general questions on programming to test your overall skills or may try and test your knowledge on big data, SQL, Python or R. Listed are a couple of questions that may turn out to be relevant for you to crack that interview like a pro.

  • List the pros and cons of working with statistical software.
  • How do you create an original algorithm?
  • If you were to contribute to an open-source project, how would you do it?
  • Name your favorite programming languages and explain why do you feel comfortable working in them.
  • What is the process of cleaning a dataset?
  • What is the method you would take for sorting a large list of numbers?
  • How does MapReduce work?
  • What is Hadoop Framework?
  • If you are given a big dataset, explain how would you deal with missing values, outliners and transformations.
  • List the various data types in Python.
  • How would you use a file to store R objects?
  • If you were to conduct an analysis, would you use Hadoop or R, and why?
  •  Explain the process using R to splitting a continuous variable into various groups in R.
  • What is the function of the UNION?
  • Explain the most important difference between SQL, SQL Server, and MSQL?
  • If you are programming in SQL, how would you use the group functions?

While a Data Science Course will teach you the basics of modeling, at an interview you may be asked technical questions like building a model, your experiences, success stories and more.

  • What is a 5-dimensional data representation?
  • Describe the various techniques of data visualisation.
  • Have you designed a model on your own? If yes, explain how.
  • What is a logic regression model?
  • What is the process of validating a model?
  •  Explain the difference between root cause analysis and hash table collisions.
  • What is the importance of model accuracy and model performance while working on a machine learning model.
  • Define the term- exact test.
  • What would you rather have; more false negatives than false positives and vice versa?
  • Would you prefer to invest more time in designing a 100% accurate model, or design a 90% accurate model in less time?
  • Under what circumstances would a liner model fail?
  •  What is a decision tree and why is it important?

Problem Solving
Most interviewers will try and test your problem-solving ability during a data science interview. You may be asked trick questions or be subjected to topics that evoke your critical thinking abilities. Listed are some questions that will help you prepare for an upcoming interview.

  • How would you expedite the delivery of a hundred thousand emails? How would you track the response for the same?
  • How would you detect plagiarism issues?
  • If you had to identify spam social media accounts, how would you do so?
  • Can you control responses, positive or negative to a social media review?
  • Explain how would you perform the function of clustering and what are the challenges you might face while doing so.
  • What is the method to achieve cleaner databases and analyze data better?
    For more such articles, feel free to click on the below link:
    How To Build A Career in Data Science?

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