概率，期望，假设检验，置信区间等等的公式和相关应用。

Q1. Explain what regularization is and why it is useful.

Which data scientists do you admire most? which startups?

Q3. How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression.

Q4. Explain what precision and recall are. How do they relate to the ROC curve?

Q5. How can you prove that one improvement you've brought to an algorithm is really an improvement over not doing anything?

Q6. What is root cause analysis?

Q7. Are you familiar with price optimization, price elasticity, inventory management, competitive intelligence? Give examples.

t2: Keep on collecting. get the book and contact lb.
8. What is statistical power?

9. Explain what resampling methods are and why they are useful. Also explain their limitations.

10. Is it better to have too many false positives, or too many false negatives? Explain.

11. What is selection bias, why is it important and how can you avoid it?

Bonus Question: Explain what is overfitting and how would you control for it

Q12. Give an example of how you would use experimental design to answer a question about user behavior.