Data Analysis Techniques (838F3)

Key facts

Details for course being taught in current academic year
Level M  -  15 credits  -  autumn term

E-learning links

Study Direct: 838F3 (09/10)

Resources

Timetable Link
Reading list
course web pages

Course description

Course outline

To introduce the mathematical and statistical techniques used to analyse data. The
course 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.
Non-linear least squares fitting.

Learning outcomes

By the end of the course, the student should
- 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.

Assessments

Resit mode of assessment

Timing

Submission deadlines may vary for different types of assignment/groups of students.

Weighting

Coursework components (if listed) total 100% of the overall coursework weighting value.

Teaching methods

How to read the week pattern

The numbers indicate the weeks of the term and how many events take place each week.

Contact details

Dr Elisabeth Falk

Assess convenor
http://www.sussex.ac.uk/physics/profile109371.html