Environmental systems are controlled by multiple factors coming from outer space, atmosphere, ocean, earth systems. We want to understand many existential questions that are related to environmental systems. What is happening with our current environmental system? Is there an alternative system that we can live on? How can we learn from other planetary
systems? How can we prevent ourselves from being destroyed or becoming extinct? To answer these questions, Environmental Scientists and Data Scientists will face the challenge of making decisions based on their understanding of the environmental systems and the abundant availability of data. We will explore remote sensing techniques, spectroscopy imaging, planetary atmosphere, astrochemistry, habitability, among other things. The course encourages students to think critically and reason quantitatively about an environmental problem rather than just focusing on getting a specific answer. Students will learn to read scientific articles, to synthesize data and to use statistical models to provide answers with estimates of uncertainty that are critical for decision makers. The course will also focus on visualizing data, communicating results in a way that allows stakeholders to make decisions and the public audience to understand. This course will have hands-on practical work with real data, R or Python, statistical and/or machine learning software packages. This course is an integrated course that combines knowledge that students acquired in different sub-fields disciplines: science, computer science, and writing. This course is therefore crucial for students to conduct a throughout (?) research project from starting with finding data source to writing up their finding. For graduate students, research skills such as data science project design and implementation will be emphasized.
