To give a theoretical and practical understanding of the statistical principles involved in the analysis and interpretation of data. To give practice in analysing data by computer program. To show how to write code to solve problems in data analysis.

(LO1) Knowledge of experimental errors and probability distributions

(LO2) The ability to use statistical methods in data analysis

(LO3) The ability to apply statistical analysis to data from a range of sources

(LO4) Using statistical information to determine the validity of a hypothesis or experimental measurement

(LO5) The ability to write code to analyse data sets

(S1) Problem solving skills

(S2) Numeracy

(S3) Digital scholarship participating in emerging academic, professional and research practices that depend on digital systems

(S4) IT skills

The course will cover: Understanding how to summarise data in a few numbers and relations between variables: Mean, variance and covariance Probability theory and how to combine probabilities : Set theory and Bayes’ Theorem Probability density functions Specific probability distributions: Binomial, Poisson and Gaussian Errors and estimators: Statistical and systematic errors and the Central Limit Theorem Corrections to estimators and qualities of good estimators Fitting a distribution to data: Likelihood functions and the principle of maximum likelihood Least square fitting methods Confidence levels Hypothesis testing

Teaching Method 1 - Lecture
Description: Lectures delivered on campus.
Attendance Recorded: Yes

Teaching Method 2 - Computing Classes
Description: Workshops to help the students solve the worksheets by computer
Attendance Recorded: Yes