One technical one product analysis question.
Data Scientists Interview Questions
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How would you measure the “feed” success? (What KPIs)
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SQL question that involved window functions
Assumption of Linear Regression, etc.
Statistics - performance metrics, Why GBM, why not xgboost , Differences between GBM and xgboost Then bias, overfitting ,underfitting, Regularisation , Lasso regression( explain). ... Followed by extended questions
Describe your previous projects
write a code in R/SQL: Given a table with three column, (id, category, value) and each id has 3 or less category (price, size, color). Now, how can I find those id's for which the value of two or more category matches to one another? For eg: ID1 (price 10, size M, color Red), ID2 (price 10, Size L, Color Red) , ID3 (price 15, size L, color Red) Then the output should be two rows: ID1 ID2 and ID2 ID3
- What is over-fitting? How do you avoid it? - What types of regularization do we have? Which one is simpler to use? L1 or L2? - Explain decision trees? What are different metrics to classify dataset? - What is bagging? - We have two models, one with 85% accuracy, one 82%. Which one do you pick? - What is p-value and how can we use it?
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