Retail Analysis Challenge
This post introduces a quick challenge intended to help data science candidates prepare for job interviews. It includes an associated Github repository with data for the challenge.
Let’s imagine that a large retailer hires you as a freelance data scientist to analyze trends from their brick-and-mortar stores. They want to identify patterns in shopping behaviors that they can use to increase their revenues.
The client doesn’t know much about data science or statistics, so they’re trusting you to bring your know-how to the table. That is to say, they don’t have a specific approach in mind. They’re only interested in business outcomes.
When they present you with the data, you’re given a file with many baskets. Each basket represents the items in a user’s shopping cart at the time of checkout.
The Challenge
Analyze the data and report on your findings.
What methods did you use? What recommendations do you have for the client? What additional information would you like to see?
Logistics
I’ve made the challenge dataset available on Github.
You can send your findings to me via email at: daniel (dot) isaza (dot) 93 (at) gmail
If you’re onto something cool, I may ask if I can give you a shoutout on the Weekly Data Science Interview mailing list. And be sure not to miss my walkthrough of my own analysis, which I’ll be sending out on 7/14 (join here for $1/week).
