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The analysis of data arising in Bioinformatics and Biostatistics requires the use of sophisticated statistical techniques and computing packages. This subject introduces the theory underlying modern statistical inference and statistical computation. Both classical and Bayesian statistical methods are developed and many standard statistical methods are included as applications of a common theory. This subject is co-taught with MAST20005 Statistics.
Intended learning outcomes
- Students completing this subject should be familiar with the basic ideas of estimation and hypothesis testing and be able to carry out many standard statistical procedures using a statistical computing package.
- Students should develop the ability to fit probability models to data by both estimating and testing hypotheses about model parameters.
- problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
- analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
- collaborative skills: the ability to work in a team;
- time management skills: the ability to meet regular deadlines while balancing competing commitments;
- become familiar with a major statistical computing package.
Last updated: 2 December 2019