Teaches a variety of statistical techniques commonly used to analyze environmental data sets and quantitatively address environmental questions with empirical data. Covers fundamental statistical concepts and tools, including sampling and study design, linear regression, inference, and time series analysis, as well as foundational concepts of spatial and space-time dependency and associated impacts on inference.

No Prerequisites

4

Units

Letter

Grading

1, 2, 3

Passtime

Graduate students only

Level Limit

Environmental science

College
These majors only eds
CZAPANSKIY M
No info found
BREN 3022
T
12:30 PM - 13:50 PM
5 / 36

Fall 2024 . Czapanskiy M
BREN 1424
T R
09:30 AM - 10:45 AM
See All
EDS 222 Czapanskiy M Fall 2024 Total: 30
EDS 222 Carleton T A Fall 2023 Total: 36
EDS 220
5 / 36 Enrolled
Working with Environmental Data
Galaz-Garcia
T R
09:30 AM - 10:45 AM
86.9% A
EDS 223
7 / 36 Enrolled
Geospatial Analysis and Remote Sensing
Adams A R
T R
08:00 AM - 09:15 AM
91.1% A
EDS 242
5 / 32 Enrolled
Ethics & Bias in Environmental Data Science
T B A
M
12:30 PM - 13:45 PM
100.0% A