Course Catalog

Internships may be taken for 0 credits. Students may do more than one internship, but internship credit cannot cumulatively total more than 4 credits.

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.

This project-based course introduces data science by looking at the whole cycle of activities involved in data science projects. Students will learn how to think about problems with rigor and creativity, ethically applying data science skills to address those problems. The course project will address the theoretical, mathematical and computational challenges involved in data science.

The 21st century has seen a big increase in the amount of data which is made accessible. Social media such as Facebook, online shops such as Amazon and many others, are all gathering raw data. But what can be done about this data? Data Science covers tools and methods around the extraction of knowledge from data. Such tools cover its collection, storing, processing and analysis. In this course we will learn about several of the most important tools in the above flow and will apply them to real-world examples.