Data Analysis Techniques (890F3)
Data Analysis Techniques
Module 890F3
Module details for 2024/25.
15 credits
FHEQ Level 7 (Masters)
Module Outline
To introduce 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: Random variables; error propagation; estimation and fitting; Bayesian probabilitry; Monte Carlo techniques.
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 and Bayesian statistics.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 70.00% | |
| Coursework components. Weighted as shown below. | ||
| Software Exercise | A1 Week 1 | 100.00% |
| Coursework | 30.00% | |
| Coursework components. Average of best 2 coursework marks. | ||
| Problem Set | T1 Week 10 | |
| Problem Set | T1 Week 4 | |
| Problem Set | T1 Week 7 | |
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.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Autumn Semester | Workshop | 1 hour | 11111111111 |
| Autumn Semester | Lecture | 1 hour | 22222222222 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
Prof Jonathan Loveday
Assess convenor, Convenor
/profiles/114680
Prof Matthias Keller
Convenor
/profiles/178720
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