Description: This course is intended for M.S. and Ph.D. students enrolled in the SMS/SMAST Marine Science and Technology program. The course teaches students how to use the Matlab technical computing software to perform a variety of statistical and data analysis tasks common to estuarine, ocean, and atmospheric sciences. As part of the course, students apply standard data analysis methods to real data in a way that teaches them how to handle data and perform analysis, and increases their understanding of fundamental physical and biogeochemical processes in the ocean. Emphasis is less on theoretical proofs, and more on a hands-on application of methods to various problems. Where possible, example applications are based on individual student's research needs. This course is intended for any students who require a working understanding of Matlab and basic data/model analysis techniques.

Prerequisites: Consent of instructor.

Evaluation:

• 40% labs/homework assignments and class participation
• 30% midterm project presentation
• 30% final project presentation

Matlab website: http://www.mathworks.com/ http://www.mathworks.com

Specific Matlab online documentation:
http://www.mathworks.com/access/helpdesk/help/techdoc/matlab.html

Tentative Lecture Schedule

• Linear algebra and calculus review
• Introduction to Matlab programming software / Best Programming Practices
• Data handling in Matlab (e.g., loading and manipulating data, input/output file formats, performing basic calculations - examples: data of student's choice)
• Computing basic statistics (incl., mean, var, stddev, pdfs, normality, outlier analysis)
• Observations vs. Models (incl. discretization, subsampling, noise/uncertainty; - examples: advection/diffusion modeling and/or other equation solving)
• Data Visualization in Matlab (incl. plotting, handle graphics, & other methods for gridding and smoothing - examples: wave visualization / propagation)
• Intro to the Statistics Toolbox (incl. ANOVA, and multivariate statistics; - examples: harmonic analysis)
• Correlation analysis, lagged correlations (examples: DO vs. light data)
• Intro to the Signal Processing Toolbox (examples: working with DO, Chl-a, stage data)
• Gridding and Interpolation Methods (1-, 2-, and 3-D; - examples: water quality station data)
• Methods for non-stationary means (incl. kriging, complex demodulation)
• Principle Component Analysis / Empirical Orthogonal Functions
• Image processing/analysis (examples: lab image data;
GIS/land use)
• Student's choice - topics by request based on student project / research needs
• Presentations of Final Projects