MAS8404 : Statistical Learning for Data Science
- Offered for Year: 2019/20
- Module Leader(s): Dr Wentao Li
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
|Semester 1 Credit Value:||10|
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
- Principal components analysis
|Guided Independent Study||Assessment preparation and completion||15||1:00||15:00||Coursework exercises|
|Scheduled Learning And Teaching Activities||Lecture||9||2:00||18:00||Lectures|
|Guided Independent Study||Assessment preparation and completion||1||0:30||0:30||Oral Examination|
|Guided Independent Study||Assessment preparation and completion||5||0:30||2:30||Preparation for Oral Examination|
|Scheduled Learning And Teaching Activities||Practical||9||2:00||18:00||Practical sessions|
|Guided Independent Study||Directed research and reading||19||1:00||19:00||Background reading|
|Guided Independent Study||Project work||18||1:00||18:00||Project|
|Guided Independent Study||Independent study||9||1:00||9:00||Lecture follow-up|
Teaching Rationale And Relationship
Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Practicals are used both for solution of problems and work requiring extensive computation and to give insight into the ideas/methods studied; they are also used to discuss the course material, identify and resolve specific queries raised by students and to allow students to receive individual feedback on marked work. Office hours provide an opportunity for more direct contact between individual students and the lecturer.
The format of resits will be determined by the Board of Examiners
|Practical/lab report||1||M||45||Up to 3 practical reports Word count: Up to 1,000 words as specified for each report.|
|Report||1||M||55||Project report Word count: Up to 1,500 words|
Zero Weighted Pass/Fail Assessments
|Oral Examination||M||A structured discussion including a software demonstration and reflection on the key learning objectives of the coursework project.|
Assessment Rationale And Relationship
Written assignments (approximately 3 pieces of work of approximately equal weight) followed by a larger piece of project work allow the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback; the smaller pieces of work are thus formative as well as summative assessment.
The semi-structured interview facilitates a reflective discussion about how individual students have met the learning objectives of the module and how the principles of fundamental statistics are embedded in the functionality of their project work.