README.md

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.

Software

• Git reference

• Installing software

Course Schedule

Week 1

• Introduction to sampling and statistics
• The command line and shell scripts - Software Carpentry tutorial

Week 2

• Probabilities and Distributions
• Python Introduction
• Exercises - Python Introduction

Week 3

• Modeling, sampling, confidence intervals
• Boolean logic in Python
• Cruise data analysis using Pandas
• Exercises

Week 4

• Hypothesis testing, power analysis
• Loops in Python
• Python functions
• Exercises

Week 5

• Correlation, general least squares regression
• Implementing linear regression in Python
• Conditional execution
• Exercises

Week 6

• Multivariate regression
• Python modules

Week 7

• Analysis of variance (ANOVA)
• Non-parametric statistical tests
• The generalized linear model
• Statistics example problems
• Example: comparing temperature in three regions

Week 8

• Error propagation
• Poisson regression example
• Optimization and nonlinear modeling
• Population growth and optimizing exponential fits

Week 9

• Principal component analysis (PCA) and empirical orthoginal functions
• Multidimensional scaling analysis
• Example: NDBC wind
• Example: Monterey Bay Kelp PCA

Week 10

• Week 10 - Spectral analysis
• Week 10 Lab - Elkhorn Slough spectral analysis - Part 1

Week 11

• Week 11 Lab - Elkhorn Slough spectral analysis - Part 2

Week 12

• Week 12 - Spatial analysis
• Week 12 - Interpolation
• Week 12 - Mapping examples

Week 13

• Week 13 - Convolution and filtering
• Week 13 - Image analysis

Week 14

• Week 14 - Python packages