- Explain what regularization is and why it is useful.
- Which data scientists do you admire most? which startups?
- How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression.
- Explain what precision and recall are. How do they relate to the ROC curve?
- How can you prove that one improvement you've brought to an algorithm is really an improvement over not doing anything?
- What is root cause analysis?
- Are you familiar with price optimization, price elasticity, inventory management, competitive intelligence? Give examples.
- What is statistical power?
- Explain what resampling methods are and why they are useful. Also explain their limitations.
- What is selection bias, why is it important and how can you avoid it?
- What do you mean by word Data Science?
- Explain the term botnet?
- What is Data Visualization?
- Why data cleaning plays a vital role in analysis?
- What is Linear Regression?
- What do you understand by term hash table collisions?
- Compare and contrast R and SAS?
- What do you understand by letter ‘R’?
- What is the goal of A/B Testing?
- What is an Eigenvalue and Eigenvector?
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