Module Catalogue 2024/25

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

Aims

More data than ever before are being generated and stored, in a variety of fields across industry. The term “big data" has emerged in acknowledgement of the vast amounts of data now available. By applying statistical analyses to these data sets, we can start to use them to answer important questions such as (i) which are the important factors affecting the quality of an industrial process; (ii) how many different types of customer are interested in your product. Commonly the data sets that arise in industry are multivariate, comprising a large number of observations on many variables. In this module we study how we can learn from data sets of this form. There is an emphasis on hands-on application of the theory and methods throughout, with extensive use of R.
Specifically, the module aims to equip students with the following knowledge and skills:
-       To gain an overview of modern statistical approaches to learning from data.
-       To gain experience in the application of these techniques to the analysis of large and complex data sets across a range of application areas in industry.

Outline Of Syllabus

-       Linear regression, including variable selection and regularisation (ridge regression, the lasso and the elastic net)
-       Classification including linear discriminant analysis and logistic regression
-       Generalized linear models
-       Tree-based methods, including regression trees, classification trees and random forests
-       Clustering
-       Principal components analysis

Intended Knowledge Outcomes

At the end of the module, students will be familiar with a range of statistical models and methods for analysing big data. They will be familiar with the strengths and weaknesses of these methods.

Intended Skill Outcomes

At the end of the module, students will be able to: use R to analyse large data sets using statistical learning techniques, and interpret the results; identify the appropriate statistical technique to use in a wide variety of real-life problems.

Teaching Rationale And Relationship

Lectures and set reading are used for the delivery of theory and explanation of methods, illustrated with examples. Practicals are used both for solution of problems and work requiring extensive computation and to give insight into the ideas/methods studied. There are two present-in-person practical sessions per week to ensure rapid feedback on understanding. Scheduled online drop-ins provides opportunity for students to ask questions and receive immediate feedback.

Assessment Rationale And Relationship

A compulsory formative practical report allows the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback, before the summative assessments.

The oral presentation encourages students to focus on interpretation of statistical results, builds their skills in the presentation of statistical concepts, and provides opportunity for feedback.

In a foundational subject like the Mathematical Sciences, there is research evidence to suggest that continual consolidation of learning is essential and the fewer pieces of assessment there are, the more difficult it is to facilitate this. On this module, it is particularly important that the material on the earlier summative assessment is fully consolidated, before the later assessment is attempted.

General Notes

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