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sdr2015 HW3, due Sun Oct 18 at 5pm
Social Data Revolutionaries,
Hope you had an amazing class this week!
To help you get  the most out of the next class, this is the quick homework assignment.
Anything else? Shoot me a quick email.
Looking forward to seeing you on Tuesday at 3:30!
Andreas
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Preparation for Class 4  (Applications of Social Data to Finance -- Oct 20, 2015)
Lenders use a variety of variables to predict borrower creditworthiness. They range from standard information such as current income, length of credit history, and percentage of on-time payments, to non-standard information such as education and social network data. Some pieces of data may prove to be very predictive, but also controversial and even unethical.
Describe a data source that might be predictive of a borrower’s likelihood of paying back a loan but could be considered unethical.
E.g., Ethnicity.
What proxy might correlate well with that controversial variable that is socially more acceptable?
E.g., ZIP code as proxy for race.
One question you want to ask the guests:
General Feedback
How can we leverage our guest speakers to give you maximum value?
E.g., Depth vs breadth, more/less personal stories, more/less technical.
How about the amount of guidance given to the guest speakers?
Speakers need more structure
Speakers need more freedom
Clear selection
Anything else?
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