Physics and astronomy

Data Analysis Techniques

Module code: 890F3
Level 7 (Masters)
15 credits in autumn teaching
Teaching method: Workshop, Lecture
Assessment modes: Coursework, Open examination

This module introduces you to the mathematical and statistical techniques used to analyse data. The module is fairly rigorous, and is aimed at students who have, or anticipate having, research data to analyse in a thorough and unbiased way.

Topics include: probability distributions; error propagation; maximum likelihood method and linear least squares fitting; chi-squared testing; subjective probability and Bayes' theorem; monte Carlo techniques; and non-linear least squares fitting.

Pre-requisite

None.

Module learning outcomes

  • Understand various probability distributions, such as Binomial, Poisson and Gaussian, and be able to apply them appropriately.
  • Be able to propagate uncertainties in experimental (or theoretical) calculations, including use of the covariance matrix to treat correlations.
  • Understand and be able to apply various parameter optimization techniques such as Least Squares fitting and the Maximum Likelihood method.
  • Be familiar with the use of Monte Carlo techniques.