Quantitative Interview Preparation Guide, updated version here ==>
A short list of resources and topics covering the essential quantitative tools for data scientists, AI/machine learning practitioners, quant developers/researchers and those who are preparing to interview for these roles.
At a high-level we can divide things into 3 main areas:
Depending on the type of roles, the emphasis can be quite different. For example, AI/ML interviews might go deeper into the latest deep learning models, while quant interviews might cast a wide net on various kinds of math puzzles. Interviews for research-oriented roles might be lighter on coding problems or at least emphasize on algorithms instead of software designs or tooling.
A minimalist list of the best/most practical ones:
Here is a list of topics from which interview questions are often derived. The depth and trickiness of the questions certainly depend on the role and the company.
Under topic I try to add a few bullet points of the key things you should know.
The bare minimum of coding concepts you need to know well.
Just to spell things out
Solving probability interview questions is really all about pattern recognition. To do well, do plenty of exercise from this and this. This topic is particularly heavy in quant interviews and usually quite light in ML/AI/DS interviews.