Overview

This module provides a comprehensive introduction to the tools and techniques used in marine science data acquisition and analysis. Students will explore a variety of topics, including oceanographic instrumentation, time-series analysis, and the use of ArcGIS for spatial data analysis. The course also covers the utilization of satellite and reanalysis data, as well as of global floats and drifter datasets, which are essential for understanding ocean dynamics.

In addition, the module presents the analysis of fishing and fisheries data, reef surveys, and operational oceanography, providing a broad perspective on marine science applications. Numerical modelling is a key component throughout the module, offering students the opportunity to apply theoretical knowledge to practical scenarios.

By the end of the course, students will have gained valuable skills in handling and interpreting diverse oceanographic data, equipping them for advanced graduate-level study or careers in marine science. This module is designed to be both informative and hands-on, ensuring that students can effectively apply their learning to real-world oceanographic challenges.

Topics typical include:

Introduction to oceanography instrumentation Time-series analysis ArcGIS Satellite and reanalysis data Global floats and drifter datasets Fishing and fisheries data Reef surveys Operational Oceanography

Numerical modelling forms an integral theme throughout much of the module content.

Assessment Strategy

Threshold(50%>):
Basic familiarity with major ocean datasets; can distinguish good data from bad; and has a basic grasp of common data analysis and presentation techniques, and of ocean numerical modelling.

Good (60%>): Broad understanding of the creation and use of major ocean datasets; ability to confidently and consistently distinguish good data from bad; and has a proficiency in common data analysis and presentation techniques, and of ocean numerical modelling.

Excellent (70%+): Deep, comprehensive understanding of the creation and use of major ocean datasets; highly developed abilities in terms of data quality control; and advanced skills in common data analysis and presentation techniques and of ocean numerical modeling

Learning Outcomes

• Be able to apply advanced quantitative methods to oceanographic data to extract and subsequently interpret spatial or temporal signals.

• Be able to critically assess the quality of data on the basis of a deep knowledge and understanding of the processes involved with ocean data acquisition or generation.

• Be able to make quantitative predictions about the physics, chemistry and biology of coastal waters by solving the governing advanced equations, numerically.

• Be able to present, integrate, and analyse spatial marine data to a professional standard