Conservation approaches have now entered the age of databases, remotely sensed data, computational modeling, and datasets based on long term monitoring. The culmination of these rich data sources is a series of fascinating papers that mine these data to address compelling, big-picture questions about how species, communities, and ecosystems respond to environmental and anthropogenic changes. These studies provide the resources to guide conservation decisions and policies with insight and deliberation. This course will take a critical approach to examining the outcomes of data science approaches to conservation. Students will evaluate data sources, methodologies, and questions addressed in some of the most exciting (and sometimes controversial) papers coming out over the last several years. In the last quarter of the class, students will identify best practices and approaches to databased conservation as well as the next set of questions that can be addressed in this manner. Learning Objectives: -Evaluate data sources for performing data science-based conservation -Critique conservation data science methods -Identify best practices for evaluating, cleaning, and accounting for bias in datasets -Recognize strategies to effectively communicate scientific results in high impact papers -Brainstorm new questions to be addressed using available datasets.