Recently I was considering whether to introduce some CS style algorithmic interview questions into our Data Science hiring loop, since having an understanding of algorithms and data structures can be useful for Data Scientists. Not having done this soft of interview for a few years I picked up my copy of Daily Coding Problem and starting solving a few problems to refresh my feeling for what it feels like as a candidate, and whether it would give us any useful signals.

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Given a biased coin, construct a fair coin.

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I discuss 4 uses of entropy in Data Science: (i) cross entropy as a loss function for training neural network classifiers (ii) entropy as a splitting criterion for building decision trees (iii) entropy for evaluating clustering algorithms (iv) entropy for understanding relationships in tabular data.

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What's the difference between cross entropy, joint entropy, conditional entropy and relative entropy?

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What's the entropy of a fair coin toss? What if the coin almost always returns Heads? My recruiter reports that very few candidates can answer these entropy related DS screening questions.

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I use PyMC3 to solve the food delivery toy problem and explore some alternative priors.

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I was grabbing a burger at Shake Shack, Mall of the Emirates in Dubai, when I noticed this notebook on the counter. The staff is using it to track food deliveries and each service (Carriage, Talabat, UberEats, Deliveroo) has its own column with the order numbers. Let's assume this is the only page for the day, and ask ourselves: given this data, what is the probability that UberEats is the most popular food delivery service?.

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The Collatz conjecture is a conjecture in mathematics that concerns a sequence defined as follows: start with any positive integer n. Then each term is obtained from the previous term as follows: if the previous term is even, the next term is one half the previous term. If the previous term is odd, the next term is 3 times the previous term plus 1. The conjecture is that no matter what value of n, the sequence will always reach 1.

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