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Description
This module aims to provide a general background on fundamental statistical methods and applications in data science, sufficient to follow other taught postgraduate level modules in Statistical Science. It is primarily intended for students registered on the MSc Data Science and MSc Data Science and Machine Learning degree programmes.听For these students, the academic prerequisites for this module are satisfied via successful admission to their programme.
Intended Learning Outcomes
- be able to prepare data for performing data analysis;
- be able to translate听data analysis problems into statistical models;
- be able to interpret听the validity of models inferred from data;
- have an听understanding of the role of computation in statistical inference;
- be able to set听up and assess听the adequacy of predictive models.
Applications - The statistical methods introduced are very general and are used in almost all areas in which statistics is applied. The module will cover applications in the context of business, social sciences, and biology, among others.
Indicative Content - Exploratory data analysis: basic visualisation for data preparation and modelling strategy. Review of probability models, in the context of the different statistical methods discussed in the module. Hypothesis testing and confidence intervals: methods for assessing the uncertainty in the analysis. Regression: linear and non-linear methods for explaining outcomes. Point estimation, maximum likelihood and basic optimization: fitting generic statistical models. Dimensionality reduction: explaining the variability in datasets using fewer dimensions.
Key Texts - Available from .
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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