This course will teach you the basis of data driven urban research for decision and policy making: the major concepts, tools, and techniques for what informatics can do for cities. You will acquire basic computational skills, basic knowledge of statistical analysis, error analysis, data acquisition and management, integration, and analytics, working with large datasets and understanding data sources, visualization, machine learning and data science, with topics touching on how to best handle spatial-temporal data, GIS, issues related to citizen science and participatory sensing, instrumentation, physical sensors, imagery, and mobile sensing platforms, issues of data ethics, privacy.
After this class you should be able to formulate a question relevant to Urban Science, find appropriate data to answer the question, prepare and analyze the data, get an answer, and communicate your answer, and your confidence level in the answer, to hypothetical stakeholders in the policy domain.
Basic knowledge of statistic and basic coding skills (ideally in python) are required.
The class is project based: all assignments will be mini-projects to be worked in groups. As you work, focus on your strengths, not your weaknesses. I am are here to help you develop the skills you do not yet have and strengthen the skills you already have. Everyone's strengths can be leveraged in the work of a group. Be supportive and focus on collaborating instead of competing.
There are very few rules. Please do not break them!