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MAS8404 : Statistical Learning for Data Science

  • Offered for Year: 2022/23
  • Module Leader(s): Dr Steffen Grunewalder
  • Owning School: Mathematics, Statistics and Physics
  • Teaching Location: Newcastle City Campus
Semester 1 Credit Value: 10
ECTS Credits: 5.0


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

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Structured Guided LearningLecture materials91:3013:30Non synchronous online pre-recorded lectures and set reading
Guided Independent StudyAssessment preparation and completion110:0010:00Formative report
Scheduled Learning And Teaching ActivitiesPractical62:0012:00Present in person structured synchronous practical
Guided Independent StudyProject work147:0047:00Main project
Scheduled Learning And Teaching ActivitiesDrop-in/surgery41:004:00Present in person drop-in
Guided Independent StudyIndependent study91:3013:30Lecture follow-up/background reading
Teaching Rationale And Relationship

Pre-recorded 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 present-in-person drop-ins provides opportunity to ask questions and receive immediate feedback. Students unable to attend PiP will be able to complete the practical work at home and will be able to receive immediate feedback through joining the drop-ins virtually.

Alternatives as described will be offered to students unable to be present-in-person due to the prevailing C-19 circumstances.

Student’s should consult their individual timetable for up-to-date delivery information.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Report1M100Main module project 2000 words
Zero Weighted Pass/Fail Assessments
Description When Set Comment
Oral PresentationMA 3 min video articulating the main findings of one aspect of the coursework report
Formative Assessments
Description Semester When Set Comment
Practical/lab report1MCompulsory report allowing students to develop problem solving techniques, practise methods learnt and assess progress.
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.

Reading Lists