67-361 Big Data & Sustainability
6 units, Carnegie Mellon University
Sustainability is one of the greatest global predicaments now facing us. Symptoms abound climate change, depletion of water resources, deforestation, pollution, and on and on. There are no easy answers to these problems. Even framing the relevant questions is difficult. Choosing between competing alternatives runs the risk of addressing one problem at the expense of another. Only through thoughtful and careful analysis can informed, robust and helpful strategies be developed. Massive amounts of data are now available in a range of domains, e.g., telemetry, energy consumption, traffic patterns, healthcare etc. As a new resource, Big Data is differentiated from traditional data by its volume, velocity, and variety. The dual combination of Big Data and technological advances in the general field of analytics and data mining currently provide an opportunity to potentially answer heretofore inaccessible questions. In the domain of sustainability, advances in sensor technology, metrology, physics based modeling, epidemiology, and ecological sciences provide an explosion of data yet to be mined. Relationships among these apparently disparate data sources have yet to be established and leveraged. In this introductory course, students will gain exposure to sustainability. We will then examine some classical analytic techniques and their limitations for the nature of problems related to sustainability. We will tie these two themes of the course together by exploring contemporary Big Data approaches to the intractable problems of sustainability. Units include 1.Background on the problem domain Sustainability 2.Overview of classical Data Analytics and their limitations 3. Introduction to Big Data and associated platforms e.g. Apache Hadoop, IBM Watson 4. Exploring the potential of Big Data approaches to the problems of Sustainability. For Juniors and Seniors or by instructors approval
Prerequisites: 15-112 (min C) and 36-201 (min C)
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