This course introduces the techniques and tools used to assemble, manage, and analyze many types of data used to support decision-making in urban environments. We will work with real-world datasets. Since these datasets are often messy and incomplete, an important part of the course will be learning to clean and analyze imperfect data. The goal of this course is to prepare students to apply urban data analytics in practice. More emphasis will be placed on practical applications of urban data analytics than on the theory underlying these methods. Students will also learn good coding practices for managing urban data science projects, including version control, documentation, and modular design.
The class has no formal prerequisites, and no programming experience is required. Undergraduate students at all levels are welcome to take this course.