Interview Q&A / Data Science
Data Science interview questions
Statistics, pandas, cleaning data and exploratory analysis for analyst and data science roles.
Prefer rapid revision? Practise Data Science short interview answers →
- What are the assumptions of linear regression?
The main ones are a linear relationship, independent errors, constant error variance (homoscedasticity), roughly normal errors for inference, and no severe multicollinearity. Violations mainly hurt interpretation and confidence intervals rather than raw prediction.
IntermediateQuick answer1 min read - What is a residual, and what can a residual plot tell you?
A residual is actual minus predicted. Plotting residuals against fitted values should look like random scatter around zero; curves, fans or clusters reveal non-linearity, changing variance or missing variables.
IntermediateQuick answer1 min read - What is multicollinearity, and how do you detect it?
Multicollinearity is when predictors are strongly correlated with each other, which makes coefficient estimates unstable and hard to interpret. Detect it with a correlation matrix or variance inflation factor; VIF above about 5 to 10 is a warning.
IntermediateQuick answer1 min read