CEGE080 Data Analysis

Data Analysis
(15 credits, Term 2)

Staff: Jonathan Lliffe

Aims: Students will acquire a working and practical knowledge of the essential tools for analysing data, such that they can describe the quality and nature of given data sets. They will have sufficient knowledge of the principles of error propagation to determine expressions for the quality of derived products, be familiar with the aims and language of least squares analysis, and be able to apply this to both linear and non-linear problems.
At the end of the module, students should be able to apply statistical tests to experimental data. They should understand the generic concept of least squares, be able to form appropriate functional and stochastic models for a variety of circumstances and observational set-ups, generate quality indicators and interpret results.

Content: The course covers statistical principles and techniques for analysing data, with an increasing level of complexity and sophistication as it progresses. Essential tools such as matrix algebra and calculus are introduced in the early sessions. The course then proceeds to cover basic ideas on the nature of errors and statistical tests, and looks at error propagation and the correlation of errors in space and time. It then covers the least squares treatment of observational data, including both linear and non-linear problems, constrained solutions, and quality control procedures.

Assessment: 100% Coursework - 6 pieces