0 or 3 credits
Fall 2026 Lecture Distance Learning Laboratory Upper Division
The availability of low-cost and ubiquitous sensors in city infrastructure provides high granular data at unprecedented spatiotemporal scales. "Smart Cities" is a concept to utilize this data to provide a healthy, happy and sustainable urban ecosystem by integrating the information and communication technology, Internet of things, and citizen participation to effectively manage and utilize city's infrastructure and services. 'Data science' is an interdisciplinary field of scientific methods, processes, algorithms, and systems to extract knowledge from data in various forms. This course will introduce techniques that will allow the analysis, inference, and prediction of large-scale data (e.g., GPS trajectories, individual social networks, etc.) that are present in urban networks. The course will focus both on the methods and their application to smart city problems. Python will be used to demonstrate the application of each method on datasets available to the instructor. Examples of applications that will be discussed as an example of applications of data science for smart city applications include: ridesharing platforms, energy modeling, smart and energy-efficient buildings, evacuation modeling, decision-making during extreme events, and urban resilience.
Learning Outcomes1Understand the different types of data generated by smart cities.
2Understand the basics of various data mining techniques.
3Understand what type of data mining analysis is appropriate for various smart city applications.
4Gain basic knowledge of using Python for data analytics and results visualization.
5Apply the methods and techniques learned in this course to an applied smart city project.
Restrictions
made by Purdue students.