Data Scientist Interview Questions & Career Resources (2026)

"What is the difference between correlation and causation?"

Correlation shows a statistical relationship between variables. Causation means one variable directly causes change in another. Correlation does not imply causation - both could be caused by a third variable.

"What is regularization and why use it?"

Regularization adds a penalty term to the loss function to prevent overfitting. L1 (Lasso) can zero out coefficients for feature selection. L2 (Ridge) shrinks coefficients toward zero but keeps all features.

"What is the difference between supervised and unsupervised learning?"

Supervised learning uses labeled data to predict outcomes - classification and regression. Unsupervised learning finds patterns in unlabeled data - clustering and dimensionality reduction.

"What is feature engineering and why is it important?"

Feature engineering creates new input features from existing data to improve model performance. Often more impactful than algorithm selection. Good features capture domain knowledge and patterns the model can't learn on its own.

"What is AUC-ROC and how do you interpret it?"

AUC-ROC measures a classifiers ability to distinguish between classes across all thresholds. AUC of 0.5 means random guessing, 1.0 means perfect classification. Higher AUC indicates better model discrimination.