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Data Analysis in Water Sciences
Overview
Syllabus
Course Project (CEWA 565)
Modules
1) Python, Statistics Review
Lab 1-1: Python, Jupyter, and Plotting
Lab 1-2: Probability Distributions and Statistics
Lab 1-3: Empirical Probability Distributions
Lab 1-4: Probability distributions and random numbers
Homework 1
2) Hypothesis Testing
Lab 2-1: Hypothesis Testing
Lab 2-2: Type II Error and Power
Lab 2-3: T-tests and Tests for Change in the Variance
Lab 2-4: Monte Carlo Tests & Random Numbers
Homework 2
3) Non-Parametric Tests and Analysis of Variance
Lab 3-1: Non-Parametric Tests
Lab 3-2: Rank-Sum Test Example
Lab 3-3: Analysis of Variance (ANOVA)
Homework 3
4) Trend Analysis, Regression
Lab 4-1: Linear regression
Lab 4-2: Quantile Regression
Lab 4-3: Confidence Intervals
Lab 4-4: Mann-Kendall Trend Test
Homework 4
5) Multiple Linear Regression & Autocorrelation
Lab 5-1: Multiple Linear Regression
Lab 5-2: Autocorrelation
Homework 5
6) Bayesian Statistics
Lab 6-1: Bayes’ Theorem Example: Water Quality Testing
Lab 6-2: Bayes’ Theorem Example: River Pollution
Lab 6-3: Bayes’ Theorem with Probability Distributions
Homework 6
7) Markov Chains
Lab 7-1: Markov Chains - Basic Examples
Lab 7-2: Markov Chains - ENSO Phases
Lab 7-3: MCMC Rating Curves
Markov Chain Monte Carlo Example
Homework 7
8) SVD, Timeseries Analysis
Lab 8-1: SVD with Monthly Precipitation
Lab 8-2: Timeseries and FFT
Lab 8-3: Example timeseries analysis with FFT
Homework 8
Resources
Python
NumPy and the ndarray
More python tips
Jupyter
Data visualization
A matplotlib demo
Warming Stripes
Interactive plotting with bokeh
Other Resources
Resources for Instructors
Repository
Open issue
Index
