README.md

Data Analysis Techniques In Marine Science

MS 263 - Moss Landing Marine Labs

An introduction to using observational oceanographic data, with hands on practice in scientific programming for data analysis with Python.

These course notes are in Jupyter Notebooks. They can be viewed as static web pages on Github or run interactively on Binder.

General information

• Installing software

• Git reference

• Code of conduct

• Outside resources

• Google Drive folder (syllabus, slides, readings)

Lecture and lab notes

• Week 0 - Introduction

• Week 0 Lab - Python Intro

• Week 1 - Probabilities and Distributions

• Week 1 Lab - Python Functions

• Week 2 - Hypothesis testing, power analysis

• Week 2 Lab - Introduction to the Bash shell, conditional execution

• Week 3 - ANOVA, non-parametric statistics

• Week 3 Lab - Loops, file input, Pandas introduction

• Week 4 - Correlation, general least squares regression

• Week 4 - Python packages

• Week 4 Lab - Oceanographic cruise data

• Week 5 - Multiple regression, matrices

• Week 5 Lab - Multiple regression and transformations example

• Week 6 - Optimization and interpolation

• Week 6 Lab - Population growth and optimizing exponential fits

• Week 6 Tutorial - Git

• Week 7 - Principal Component Analysis/Empirical Orthogonal Functions

• Week 7 Lab - Monterey Bay Kelp PCA

• Week 8 - Multi-Dimensional Scaling Analysis/Correlation Functions

• Week 8 Lab - Pacific Decadal Oscillation and autocorrelation

• Week 9 - Spectral analysis

• Week 9 Lab - Elkhorn Slough spectral analysis - Part 1

• Week 9 Lab - Mapping and projections

• Week 10 Lab - Elkhorn Slough spectral analysis - Part 2

• Week 11 - Spatial analysis