Data Analysis Techniques in Marine Science#

An introductory resource for analyzing oceanographic data. Fundamental concepts in sampling and statistics are combined with hands on practice in scientific programming with Python.

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Part 1. Introduction to Python

1. Setup and software installation
2. Introduction to Python
3. Boolean logic in Python
4. Cruise data analysis in Python
5. Loops
6. Conditional execution
7. Functions
8. Creating your own Python module
9. Python packages

Part 2. Probability distributions and linear modeling

1. Sampling and statistics: Introduction
2. Probability and distributions
3. Modeling, sampling, confidence intervals
4. Correlation and linear regression
5. Implementing linear regression in Python and matrix review
6. Multivariate regression
7. Multivariate regression tutorial: aragonite saturation state
8. Poisson regression example
9. Error propagation

Part 3. Hypothesis testing and power analysis

1. Hypothesis testing and Power
2. Analysis of variance (ANOVA)
3. Non-parametric statistical tests
4. The generalized linear model

Part 4. Nonlinear modeling and optimization

1. Optimization and nonlinear modeling
2. Example: Modeling exponential growth

Part 5. Principal component analysis and related techniques

1. Principal Component Analysis (PCA) and Empirical Orthogonal Functions (EOFs)
2. Multidimensional Scaling Analysis
3. NDBC data - analysis of wind vector time series
4. Monterey Bay Kelp PCA

Part 6. Spectral analysis of time series

1. Spectral Analysis
2. Spectral analysis example
3. Spectral analysis of LOBO data in Elkhorn Slough - Part 2
4. Low pass filtering data
5. Filtering and Convolution
6. Common oceanographic filters
7. Practical implementation

Part 7. Spatial analysis

1. Spatial Analysis
2. Interpolation techniques
3. Map tutorial

Part 8. Image analysis

1. Image Analysis
2. Ways of representing an image (or color) as a number
3. Convolution in two dimensions
4. Image analysis in Python

Appendix A. Git reference

Git Reference