One of the questions at the panel interview focused on how to handle high-cardinality categorical variables in a model, especially when a naive approach could introduce noise or performance issues. The discussion was very practical and centered on trade-offs rather than theoretical tricks.
Mathematical Modeling questions
1. Case studies related to the main skills they looking for as job descriptions. 2. If you have really done the work mentioned on your resume just dive in-depth into it. 3. Be confident, a professional look are important
write a function that is being fed 2 arrays and outputs the root mean squared error for them.
Tell me about yourself?
How do you think DH makes money?
- what is collinearity? what is the difference between bagging and boosting?
ML basics, decision tree and etc. Recommendation system details
Describe a project you worked on. What can you do better with this code?
typical problems from a leetcode (not data science)
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