Interview Questions
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Explain precision, recall, and F1-score
FoundationalExplain what precision, recall, and F1-score mean. When would you optimize for one over the other?
Explain the difference between correlation and causation
FoundationalWhat's the difference between correlation and causation? Provide examples and explain how you would establish causality in practice.
Explain what a p-value is and how to interpret it
FoundationalWhat is a p-value and how should it be interpreted in the context of hypothesis testing?
What is feature engineering and why is it important?
FoundationalExplain feature engineering and provide examples of common feature engineering techniques. Why is it often more important than model selection?
Explain Python list comprehensions and their advantages
FoundationalWhat are list comprehensions in Python? Provide examples and explain when they should be used instead of traditional for loops.
Explain the different types of SQL joins
FoundationalExplain the different types of SQL joins (INNER, LEFT, RIGHT, FULL OUTER) and when you would use each one.
Assumptions underlying a linear regression model.
IntermediateDescribe the assumptions underlying a linear regression model. What happens when these assumptions are violated?
What is the Central Limit Theorem and why is it important for statistical inference?
IntermediateWhat is the Central Limit Theorem and why is it important for statistical inference?
How do you design and analyze an A/B test?
IntermediateWalk through how you would design, execute, and analyze an A/B test. What are the key considerations and potential pitfalls?
Explain the Central Limit Theorem and its importance
IntermediateWhat is the Central Limit Theorem (CLT)? Why is it important in statistics and data science?
Explain cross-validation and its types
IntermediateWhat is cross-validation? Explain different types of cross-validation and when to use each one.
Explain the bias-variance tradeoff
IntermediateExplain the bias-variance tradeoff in machine learning. How does it relate to model complexity and overfitting/underfitting?
Type I and Type II errors.
IntermediateExplain the difference between Type I and Type II errors. In a medical testing scenario, which would you consider more dangerous and why?
How do you handle imbalanced datasets?
IntermediateWhat is class imbalance? What problems does it cause, and what techniques can you use to address it?
Explain logistic regression and when to use it
IntermediateWhat is logistic regression? How does it work, and when would you use it over other classification algorithms?
Explain Random Forests and how they work
IntermediateWhat is a Random Forest? How does it work, and what makes it different from a single decision tree?
Explain L1 and L2 regularization
IntermediateWhat are L1 and L2 regularization? How do they differ, and when would you use each?
Compare Bayesian and Frequentist approaches to inference
AdvancedWhat are the fundamental differences between Bayesian and Frequentist approaches to statistical inference? When would you choose one over the other?