How to ace the interview ?
1. I'm pretty good with SQL queries, python programming and report development. LC and HackerRank are good enough to practice?
2. Need suggestions for resources on statistical techniques and data modelling.
3. The interview also includes Behavioral and Hypothetical questions. Not sure what it would be like.
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comments
1. No SQL or Leetcode, but rather simple programming questions with a statistical bent. Think patterns that may come up in the course of an analysis or how you might write some of the better known functions from pandas/plyr/etc.
2. This is a tough one as DS interviewers have quite a bit of latitude on question selection. The general approach is to introduce fairly open-ended problems to which you can apply your unique technical background. E.g. if you're a Bayesian you can steer a causal inference q toward SCMs; if you're an econometrician you can talk about quasi-experiments, etc. So I'd say the main thing is to make sure you're comfortable with your field's toolset/vocabulary for statistics. In addition to that you should probably know linear model nuts & bolts, basic inference, ML core concepts, and communicating statistics to nontechnical audiences. What's data modeling?
3. This round is usually pretty chill. I'd just make sure you can talk through a few experiences you had and map them onto standard behavioral question patterns -- e.g. times you failed, dealt with a tough interpersonal situation, gave critical feedback, etc. No traps/tricks, just trying to make sure you're somewhat thoughtful and not obviously evil.
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