Computational Data Analysis for Ocean and Earth Scientists

Module overview

The module will present a variety of different types of oceanographic, meteorological, geophysical, and remote sensing data and will explore methods for processing, analysing and modelling using Python.

This module introduces you to the essential skills in computational data analysis, specifically designed for ocean and earth scientists. As we explore a variety of methods for processing, analysing, and modelling data, you'll actively engage with Python, the leading programming language in scientific computing. Topics covered in the module include statistical distributions, correlation, hypothesis testing, regression, model selection, principal component analysis, spectrum analysis, filtering, and advanced signal processing methods. For each topic, we'll provide practical exercises designed to apply these skills to real-world scenarios, including oceanography, meteorology, climate science, geophysics, and remote sensing data, allowing for a deeper understanding how scientists leverage these methods to extract meaningful insights from data.

Learning and Teaching

Teaching and learning methods

Formal lectures: Lectures will systematically cover the main concepts and topics using PowerPoint presentations and Jupyter Notebooks. Illustrated handout materials will complement most lectures. Where relevant, lecturers' own research experience in the appropriate fields is brought into the lecturing sessions. References to the applicable chapter of course materials and/or relevant journal articles are provided to complement some of the lectures.

Practical sessions: Will exemplify the theory. Practical sessions will be computer-based exercises used to illustrate the concepts covered in the formal lectures. Computer practical sessions will use Python through Jupyter Notebooks.

A wide range of support can be provided for those students who have further or specific learning and teaching needs.

Study time
Type	Hours
Independent Study	90
Teaching	60
Total study time	150

Assessment

Assessment strategy

Assignment 1 (40%): You will write Python programs to analyse and plot Ocean and Earth science datasets using statistical methods learned. You will write short summaries of your findings and interpretations. Tests Learning Outcomes: 1, 2, and 3.

Assignment 2 (60%): You will write a substantial Python program, or a number of smaller programs, to process and analyse one or more datasets using the signal processing techniques learned. You will interpret the results of the analysis and present these as short reports including the analysis methods you have applied to the data. Tests Learning Outcomes: 1, 2, and 3.

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Assignment	40%
Assignment	60%