Data Warehouse ➔ Data Scientist Job Interview Questions and Answers

Data Scientist job interview preparation guide for freshers, professionals and experienced candidates. Explore list of Data Scientist frequently asked questions(FAQs) extracted from many real job interviews

✰ What is survivorship bias?
✰ Tell us what is Collaborative Filtering?
✰ Explain me what is Interpolation and Extrapolation?
✰ Tell me what are Recommender Systems?
✰ Do you know what are confounding variables?
✰ Please explain what are Recommender Systems?
✰ Tell me what are Eigenvalue and Eigenvector?
✰ Tell me what is Collaborative filtering?
✰ Tell me Python or R – Which one would you prefer for text analytics?
✰ Tell me what is the Law of Large Numbers?
✰ Do you know what are feature vectors?
✰ Tell me what are the types of biases that can occur during sampling?
✰ Tell me what is Linear Regression?
✰ Tell me do gradient descent methods always converge to same point?
✰ What is selective bias?
✰ Explain me what makes CNNs translation invariant?
✰ Please explain how do you overcome challenges to your findings?
✰ Tell me which technique is used to predict categorical responses?
✰ Tell me how is True Positive Rate and Recall related?
✰ Tell me how do you know which Machine Learning model you should use?
✰ Tell us what methods do you use to identify outliers within a data set?
✰ Tell us are expected value and mean value different?
✰ Tell me what is power analysis?
✰ Explain me when is Ridge regression favorable over Lasso regression?
✰ Tell me how do you work towards a random forest?
✰ Tell us what are the drawbacks of the linear model?
✰ Tell us what is root cause analysis?
✰ Tell us what is the significance of Residual Networks?
✰ What is cross-validation?
✰ Do you know the steps in making a decision tree?
✰ Tell me why do segmentation CNNs typically have an encoder-decoder style / structure?
✰ Tell us how would you go about doing an Exploratory Data Analysis (EDA)?
✰ Explain me why do you want to work at this company as a data scientist?
✰ Explain me what is logistic regression? Or State an example when you have used logistic regression recently?
✰ Tell me is rotation necessary in PCA? If yes, Why? What will happen if you don’t rotate the components?
✰ What is star schema?
✰ Explain me what tools or devices help you succeed in your role as a data scientist?
✰ What is dimensionality reduction, where it’s used, and it’s benefits?
✰ Do you know what is the goal of A/B Testing?
✰ Explain me why data cleaning plays a vital role in analysis?
✰ Do you know why is naive Bayes so ‘naive’ ?
✰ Tell us how regularly must an algorithm be updated?
✰ Do you know what is logistic regression?
✰ Tell us how has your prior experience prepared you for a role in data science?
✰ Tell us why do we use convolutions for images rather than just FC layers?
✰ Explain me what is data normalization and why do we need it?
✰ Tell me how can outlier values be treated?
✰ Can you differentiate between univariate, bivariate and multivariate analysis?
✰ Tell me how is kNN different from kmeans clustering?
✰ Tell me why is resampling done?
✰ Explain me do gradient descent methods at all times converge to a similar point?
✰ Tell us how do clean up and organize big data sets?
✰ Tell us how do you identify a barrier to performance?
✰ Tell us why do we have max-pooling in classification CNNs?
✰ Tell me how do you handle missing or corrupted data in a dataset?

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