MAS8404 : Statistical Learning for Data Science
- Offered for Year: 2024/25
- Module Leader(s): Dr Marika Asgari
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
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
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 12:00 | 12:00 | Formative exercise |
| Scheduled Learning And Teaching Activities | Lecture | 6 | 2:00 | 12:00 | Present in person lectures |
| Scheduled Learning And Teaching Activities | Practical | 6 | 2:00 | 12:00 | Present in person practical |
| Guided Independent Study | Project work | 1 | 48:00 | 48:00 | Main project |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 4 | 1:00 | 4:00 | On line drop-in |
| Guided Independent Study | Independent study | 6 | 2:00 | 12:00 | Lecture follow-up/background reading |
| Total | 100:00 |
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 Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 1 | M | 100 | Main module project 2000 words |
Zero Weighted Pass/Fail Assessments
| Description | When Set | Comment |
|---|---|---|
| Oral Presentation | M | A 3 min video articulating the main findings of one aspect of the coursework report |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Practical/lab report | 1 | M | Compulsory 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
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- MAS8404's Timetable