109 Commonly Asked Data Science Interview Questions

What is the Central Limit Theorem and why is it important? How many sampling methods do you know? What is the difference between Type I vs Type II error? What do the terms P-value, coefficient, R-Squared value mean? What is the significance of each of these components? What are the assumptions required for linear regression? There are four major assumptions: 1. There is a linear relationship between the variables, meaning the model you are creating actually fits the data, 2. The errors or residuals of the data are normally distributed and independent from each other, 3. There is minimal multicollinearity between explanatory variables, and 4. Homoscedasticity. This means the variance around the regression line is the same for all values of the predictor variable. What is an example of a dataset with a non-Gaussian distribution?