Data Analysis in Water Sciences

This website is the home of labs, assignments, and other learning resources for Data Analysis in Water Sciences (CEE 465 & CEWA 565) offered in the Civil and Environmental Engineering department at the University of Washington, Seattle.

This course covers fundamental topics related to data analysis using modern computer techniques, with applications to water sciences (but techniques are applicable to many science disciplines), including:

• Statistical inference testing and error estimation

• Linear and quantile-based regression models

• Monte Carlo simulation

• Time series analysis

• Bayes theorem

• Data visualization

Prerequisite: Any one of the following are recommended: IND E 315 Probability and Statistics for Engineers, AMATH 301 Beginning Scientific Computing, CSE 142 Computer Programming, and/or knowledge of basic statistics and computer programming (Python, R, MATLAB).

Offered: Autumn quarters

---

Overview

• Syllabus
• Course Project (CEWA 565)

Modules

• 1) Python, Statistics Review
• 2) Hypothesis Testing
• 3) Non-Parametric Tests and Analysis of Variance
• 4) Trend Analysis, Regression
• 5) Multiple Linear Regression & Autocorrelation
• 6) Bayesian Statistics
• 7) Markov Chains
• 8) SVD, Timeseries Analysis

Resources

• Python
• Jupyter
• Data visualization
• Other Resources
• Resources for Instructors