20 questions for data scientists
- 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 pricing 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.
- Is it better to have too many false positives, or too many false negatives? Explain.
- What is selection bias, why is it important and how can you avoid it?
- Give an example of how you would use experimental design to answer a question about user behavior.
- What is the difference between "long" and "wide" format data?
- What method do you use to determine whether the statistics published in an article (e.g. newspaper) are either wrong or presented to support the author's point of view, rather than correct, comprehensive factual information on a specific subject?
- Explain Edward Tufte's concept of "chart junk."
- How would you screen for outliers and what should you do if you find one?
- How would you use either the extreme value theory, monte carlo simulations or mathematical statistics (or anything else) to correctly estimate the chance of a very rare event?
- What is a recommendation engine? How does it work?
- Explain what a false positive and a false negative are. Why is it important to differentiate these from each other?
- Which tools do you use for visualization? What do you think of Tableau? R? SAS? (for graphs). How to efficiently represent 5 dimension in a chart (or in a video)?