11.S951
Senseable City: data and analytics

Prerequisites: 
Entry level coding Python and/or MATLAB. Knowledge in mathematics, physics or computer science is preferred but not required.

Since their emergence around 10,000 years ago, cities have evolved into the most magnificent and consequential artifacts in human history. Today, big data and new computational methods and tools are empowering us to study these forces quantitively for the first time in history. This course studies these core tensions of urban development, uncovering the laws of our cities explored through new methods, borrowed from experimental physics and computational sciences. The resulting equations and models hold the keys to our cities, and to our common future. 

The course’s goal will be to introduce participating students with the methodological tools needed to develop urban science projects, similar to the ones created at the Senseable City Lab. Students will be provided with a relevant toolset, needed to analyze questions about the dynamics of cities from an urban science perspective. Teams of students will demonstrate their progress in the topic through the development of an urban science research paper of publishable quality at the end of the semester.