Data is everything, and everything is data. Understanding how to use and analyze it is the most critical skill of the 21st century, particularly for the environment, which comprises a multitude of complex systems and phenomena. To drive scientific inquiry and reach sound policy choices, we need to be equipped to derive clear signals from oceans of noise and vast amounts of information that are being generated on a daily basis. A new environmental data science course, offered in Fall Semester 2020 at Yale-NUS College, will introduce students to quantitative environmental data analysis using R and give an overview of foundational and cutting-edge data science techniques.
Taught by Angel Hsu and Michiel Van Breugel, Data Science for the Environment is a gateway course for Environmental Studies students to build on skills picked up from the earlier Quantitative Reasoning module and move towards more advanced data science, research design, and quantitative analysis techniques that will support their capstones. Topics include the integration of quantitative experimental design, data management and processing, and mixed effect models. These topics have hitherto not been covered in detail, or at all, in other data science or research methods courses at Yale-NUS. Additionally, this module has a strongly applied focus on the use of particular environmental datasets (e.g., spatially-explicit nature, time-bound processes, complex systems and interaction effects, mixed method approaches), which will provide students with keys tools to handle the complexity of environmental systems.
Students are expected to complete assigned readings and exercises in advance of the lecture during the first class of the week. Some lectures will have a multiple-choice question (MCQ) readiness assessment at the start of class that covers the assigned readings. During class, students will discuss the material and work on practice questions and problems. The second class of the week will generally be team-based labs, where students work on exercises and problem sets using real environmental data. For each of the main topic areas, students work on a graded individual problem set. Lastly, students will complete an independent data analysis project.
Round 1 of course selection will take place from 13th Apr, 0900 hrs to 15th Apr, 1200 hrs. Register on ModReg: https://myedurec.nus.edu.sg/