Data Methods and Graphical Representation in Oceanography

Catalog description

Credits 3. 3 Lecture Hours. COURSE OVERVIEW. This course focuses on advanced statistical, quantitative and computational methods for analyzing oceanographic observational data. You will investigate how to extract signals from noisy time series, objectively map irregular observations and decompose multivariate variability to support robust scientific interpretation. Core concepts include spectral analysis and time series representations, objective analysis and optimal interpolation for spatial fields and modal decomposition using empirical orthogonal functions (EOF), all tied to clear and effective graphical visualization. Expect sustained, hands-on engagement through problem sets, in-class discussion and exams, with an emphasis on critical assessment of data quality, method selection and the ethics of analysis and communication. RELEVANCE AND APPLICATION. Ocean science, environmental decision-making and data-centric careers all depend on the ability to turn complex observations into reliable insight. This course equips you with practical tools to evaluate data quality, build defensible time series and spatial analyses and communicate statistical results effectively. The methods you will practice, including objective mapping, optimal interpolation, spectral analysis, EOF-based multivariate interpretation, and programming, directly support research workflows and professional tasks such as monitoring, forecasting and synthesizing observations across sensors and platforms. By emphasizing rigorous inference, transparent computation and clear graphics, the course strengthens your readiness for thesis projects and collaborative research, and it builds durable skills valued across academia, government and industry. KEY TOPICS AND THEMES. Key themes span the full analysis pipeline from instrumentation awareness and data processing to advanced inference and visualization. You will study data processing and presentation, statistical methods and error handling and spatial analysis of data fields with objective analysis and optimal interpolation. The course then develops time series analysis and spectral methods, introduces digital filtering techniques and extends to multivariate analysis using empirical orthogonal functions for interpreting coupled variability across variables. You will explore foundational and emerging computational approaches, including machine learning methods and neural networks, while practicing structured, modular programming to produce high-quality statistical graphics and maps. Throughout, you will learn to assess data quality quantitatively, select appropriate methods for specific datasets and interpret statistical quantities in a scientifically meaningful way. Prerequisite: Graduate classification or approval of instructor.