EDS 296-1S
Course Name
Advanced Special Topics in Environmental Data Science: A Climate Modeling Perspective on Big Data Techniques
As climate change becomes a more and more urgent concern, there is a pressing need for trained professionals able to understand and work with data generated from global climate models, across a wide range of industries. However, the datasets involved can be enormous - on the order of many terabytes. This makes climate data an excellent way to learn 'big data' techniques while also gaining highly marketable skills in analyzing and visualizing information from future projections generated with climate models. Students in this course will learn how to access large datasets via cloud computing services (Amazon Web Services), and become familiar with using network Common Data Format (netCDF) data and the terminology associated with the Coupled Model Intercomparison Project (CMIP). Students will also gain skills in dimension reduction techniques for data visualization and multivariate geospatial statistics including regression and covariance maps and principal component analysis.
Prerequisites
All participants are expected to (a) be familiar with the Python programming language and comfortable working in JupyterLab, (b) have a GitHub profile, and (c) have git configured on their local machine.