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Some practice case interview questions for data science

In Part 1 of my KDnuggets article, I explained what hiring managers mean when they look for critical thinking in the arena of data science and analytics. These requirements relate to the nature of data problems found in industry and business settings. The datasets are generally observational, self-selected, non-random, with hidden biases, and increasingly OCCAM (link); the business leaders have high-level objectives ("we want to increase customer loyalty"). The data scientist/analyst is the person in the "middle," trying to figure out how to make the problem precise, and solvable by a systematic analysis of available data. In Part 2, I offer some practice case interview questions, based on three recent news events the college admissions scandal IPOs of ride-sharing companies like Lyft and Uber the Blue Apron post-IPO doldrums. Long a staple of the management consulting hiring process, the case interview is a free-flowing dialogue between the interviewer and the interviewee. The interviewer is holding back some data to simulate what is known at the beginning of a data analysis process. The interviewee must be willing to probe, digging out more data, and shaping the structure of the analysis. The end product is an analytical framework. No one knows if the framework would be successful until it is implemented. Those who do well in case interviews are good at (a) thinking on their feet (b) embracing uncertainty e.g. by making appropriate assumptions (c) listening to the interviewer's hints and (d) persuading. *** As I mentioned in Part 2, the best way to practice is to form a group of 3-5 people, and interview each other. If there is enough interest, we can start a group in the comments below.

from Big Data, Plainly Spoken (aka Numbers Rule Your World) http://bit.ly/2GTZX4a
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