This course will provide students with the knowledge and tools to turn data into meaningful insights, with a focus on real-world case studies in the urban planning and public policy realm. Focusing on the latest Python software tools, the course will outline the "pipeline" approach to data science. It will teach students the tools to gather, visualize, and analyze datasets, providing the skills to effectively explore large datasets and transform results into understandable and compelling narratives. The course is organized into five main sections: 1. Exploratory Data Science: Students will be introduced to the main tools needed to get started analyzing and visualizing data using Python. 2. Introduction to Geospatial Data Science: Building on the previous set of tools, this module will teach students how to work with geospatial datasets using a range of modern Python toolkits. 3. Data Ingestion & Big Data: Students will learn how to collect new data through web scraping and APIs, as well as how to work effectively with the large datasets often encountered in real-world applications. 4. Geospatial Data Science in the Wild: Armed with the necessary data science tools, students will be introduced to a range of advanced analytic and machine learning techniques using a number of innovative examples from modern researchers. 5. From Exploration to Storytelling: The final module will teach students to present their analysis results using web-based formats to transform their insights into interactive stories.
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