Frequently Asked Data Science Interview Questions in 2024

Here’s a list of frequently asked Data Science interview questions, covering a wide range of topics on which you might be asked. These questions will help you prepare for the interview. The answers to these questions depend on the candidate’s hands-on experience and the datasets he/she has worked on. You can even check out the details of successful Spark developer with the Pyspark online training.

Frequently Asked Data Science Interview Questions:

• • What is the biggest data set that you have processed and how did you process it? What was the result?
  • Tell me two success stories about your analytic or computer science projects? How was the lift (or success) measured?
  • How do you optimize a web crawler to run much faster, extract better information and summarize data to produce cleaner databases?
  • What is probabilistic merging (AKA fuzzy merging)? Is it easier to handle with SQL or other languages? And which languages would you choose for semi-structured text data reconciliation?
  • State any 3 positive and negative aspects about your favorite statistical software.
  • You are about to send one million email (marketing campaign). How do you optimize delivery and its response? Can both of these be done separately?
  • How would you turn unstructured data into structured data? Is it really necessary? Is it okay to store data as flat text files rather than in an SQL-powered RDBMS?
  • In terms of access speed (assuming both fit within RAM) is it better to have 100 small hash tables or one big hash table in memory? What do you think about in-database analytics?
  • Can you perform logistic regression with Excel? If yes, how can it be done? Would the result be good?
  • Give examples of data that does not have a Gaussian distribution, or log-normal. Also give examples of data that has a very chaotic distribution?
  • How can you prove that one improvement you’ve brought to an algorithm is really an improvement over not doing anything? How familiar are you with A/B testing?
  • What is sensitivity analysis? Is it better to have low sensitivity and low predictive power? How do you perform good cross-validation? What do you think about the idea of injecting noise in your data set to test the sensitivity of your models?
  • Compare logistic regression with decision trees and neural networks. How have these technologies improved over the last 15 years?
  • What is root cause analysis? How to identify a cause Vs a correlation? Give examples.
  • How to detect the best rule set for a fraud detection scoring technology? How do you deal with rule redundancy, rule discovery and the combinatorial nature of the problem? Can an approximate solution to the rule set problem be okay? How would you find an okay approximate solution? What factors will help you decide that it is good enough and stop looking for a better one?
  • Which tools do you use for visualization? What do you think of Tableau, R and SAS? (for graphs). How to efficiently represent 5 dimension in a chart or in a video?
  • Which is better: Too many false positives or too many false negatives?
  • Have you used any of the following: Time series models, Cross-correlations with time lags, Correlograms, Spectral analysis, Signal processing and filtering techniques? If yes, in which context?
  • What is the computational complexity of a good and fast clustering algorithm? What is a good clustering algorithm? How do you determine the number of clusters? How would you perform clustering in one million unique keywords, assuming you have 10 million data points and each one consists of two keywords and a metric measuring how similar these two keywords are? How would you create this 10 million data points table in the first place?
  • How can you fit Non-Linear relations between X (say, Age) and Y (say, Income) into a Linear Model?
  • What is regularization? What is the difference in the outcome (coefficients) between the L1 and L2 norms?
  • What is Box-Cox transformation?
  • What is Multicollinearity ? How can we solve it?
  • Does the Gradient Descent method always converge to the same point?
  • Is it necessary that the Gradient Descent Method will always find the global minima?

Boost your interviewing skills with these set of questions and land the job of your dreams.

Edureka has a specially curated Data Science Course Online that helps you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes. You’ll learn the concepts of Statistics, Time Series, Text Mining, and an introduction to Deep Learning as well. New batches for this course are starting soon!!

Got a question for us? Please mention them in the comments section and we will get back to you.

Related Posts:

Top Data Science Interview Questions for Budding Data Scientists

Implementing k-means Clustering to Classify Bank Customers

Implementing Hadoop & R Analytical Skills in Banking Domain

Random Forest Classifier