Beginning this June, teams of students will devote 50+ hours to solving a data analytics challenge faced by your organization. Student work will include:

1) Framing the problem.

2) Acquiring and managing large data sets.

3) Applying modern statistics.

4) Identifying and implementing the necessary technology infrastructure.

5) Providing a sustainable management-level solution.

Project examples include, but are not limited to:

• Predictive analytics in ecommerce & retail: Which products/categories to market to a customer, given the customer profile?
• Predictive analytics in investments & trading: Which stocks or securities to purchase following a sequence of events?
• Pricing for new technology products & services: How to price a new tech product, based on competitive, customer, and transactional data?
• Credit scores & ratings: How to assess the credit risk of a borrower, based on the borrower profile and meta data?
• Financial fraud likelihood: What is the likelihood of fraud for a user attempting to access your personal finance solution?
• Customer segmentation & targeting: What value to assign to a customer based on the past purchase and/or transactional data and customer profile?