Environmental Data Analysis and Simulation
Understanding data is an increasingly integral part of working with environmental systems. Data analysis is an integral part of developing statistical and numerical models to understand system dynamics and project future conditions and outcomes. This course will provide an overview of a generative approach to environmental data analysis, which uses simulation from models to provide insight into the structure of data and its data-generating process. The goal is to provide students with a framework and an initial toolkit of methods that they can use to formulate and update hypotheses about data and models. Students will actively analyze and use real data from a variety of environmental systems, potentially including the climate system, sea levels, air pollution, and the electric power system.
Topics
- Create, interpret, and critique data visualizations.
- Calibrate environmental models to observations, possibly including censored and missing data.
- Simulate alternative datasets from models using statistical methods such as the bootstrap and Monte Carlo.
- Assess model adequacy and performance using predictive simulations.
- Apply and contextualize model selection criteria.
- Evaluate evidence for and against hypotheses about environmental systems using model simulations.
- Emulate computationally-complex models with simpler representations.
Official roster learning outcomes
Prerequisites & course information
background equivalent to: CEE 3040; ENGRD 2700; CS 1110 or CS 1112.
Sources
- Course catalogue ↗2026-2027
- Catalogue entry ↗
Catalogue variants
- Environmental Data Analysis and Simulation · 3.0 · Graduate