Class Goals and Structure
This is a hands-on class in methods to analyze the increasingly large data sets that are being collected in the ocean. Class will be conducted primarily in the Spatial Analysis Laboratory using MATLAB (Mathworks, Inc.) with an emphasis on applications. Students will first learn to apply basic data analysis techniques and to derive error estimates using a variety of data types. Then students will complete two group projects demonstrating their ability to apply and to adapt the methods creatively to answer a scientific question. Each project will require a summary in the format of a short journal article to be written separately by each student. Types of data include 1) fixed time series measurements (such as at a mooring), 2) Lagrangian measurements (float or glider), and 3) satellite measurements. Skills learned will include plotting, editing, filtering, interpolating and gridding data; computing and/or removing biases, trends, or periodic signals; merging different types of data; deriving error estimates; and statistical characterizations and comparisons of two types of data (linear regression, correlations, principal components/EOFs).
The course will be 4 units and will include 1 hr per week of lecture plus two 1.5-hr laboratory sessions. The lecture time will be devoted to providing background material for the exercises in the laboratory sessions and discussing examples from the literature of the analysis methods. Exercises (homework) will be provided to familiarize the students with the applications. Familiarity with Matlab (or comparable programming experience) is required. Familiarity with simple statistics, linear algebra, and oceanography is helpful.