I respectfully disagree with everyone saying these are statistics. This is analytics. Most of them are down to earth and practical matters that I think would be

I respectfully disagree with everyone saying these are statistics. This is analytics. Most of them are down to earth and practical matters that I think would be lost on someone who did statistics as a math degree (but maybe this distinction is unclear because it is also observably different from straight comp sci or neural net expertise). I recently graduated from a masters of business analytics program and would have humbly assumed that the breadth and depth of knowledge asked for in these questions would be beyond my capacity, but it was actually pretty spot on with what I had learned. Strictly speaking, I am far from an expert in statistics/math/comp sci. But these seem like reasonable questions. The best one is "How will you decide whether a customer will buy a product today or not given the income of the customer, location where the customer lives, profession and gender? Define a machine learning algorithm for this." This is a difficult question because for someone who has played with data science before, the answer seems to be obvious: throw the data into your favorite predictive algorithm and see the result (worst case: deep learning). What they are probably looking to see is a discussion of feature engineering, hypothetical exploratory analysis, and an effective way to present the answer (straight probabilities, weighted loss functions, outlier detection, etc.)