Implications of big data for data scientists and engineers

Abstract

ANALYTICS as a discipline is certainly a hot topic. As a member of an industrial engineering department, I am responsible for sourcing real world projects for undergrad senior design courses. I am proud of the types of analytics projects we have pursued for both social good and corporate good. Some the examples of projects include: 1. Optimizing the scheduling of nurse resources at a hospital 2. Optimizing the use of IMRT (radiation therapy) for cancer patients 3. Optimizing the placement of medical resources along the route of the Chicago (and Houston) marathons 4. Creating an analytics based fake news detector 5. Creating an algorithm for predicting housing prices based on historical data 6. Optimizing electric vehicle charging for city of Evanston 7. Optimizing the DIVVY bike locations for city of Evanston While these are very focused projects that my students have worked on in the past few years as part of a senior design project based course, it is clear that analytics is also exploding onto the scene of corporate America. I would characterize these projects as “small data” as the datasets are quite modest and focused. They are not usually available publically and are specific to a client scenario that we are asked to model and analyze.