Undergraduate Programme and Module Handbook 2020-2021 (archived)
Module MATH4287: High-Dimensional Data Analytics
Department: Mathematical Sciences
MATH4287: High-Dimensional Data Analytics
| Type | Open | Level | 4 | Credits | 10 | Availability | Not available in 2020/21 | Module Cap | None. | Location | Durham |
|---|
Prerequisites
- Statistical Inference (MATH2671)
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- To provide advanced methodological skills for the analysis of high-dimensional data, spanning the arc from the theoretical underpinning of the methods to the practical application on real data.
Content
- • Multivariate statistics: principal component analysis, cluster analysis, and related methods. • Dimension reduction: regularized regression techniques.
Learning Outcomes
Subject-specific Knowledge:
- By the end of the module students will: • have gained a thorough understanding and working knowledge of advanced statistical methods for multivariate data; • have gained an appreciation of the wider concepts that such methods are built on, allowing straightforward understanding of yet unseen methods; • have gained intuition to distinguish supervised and unsupervised learning scenarios, and which specific methods to apply in particular situations.
Subject-specific Skills:
- • Students will have advanced mathematical skills in the following areas: modelling, computation.
Key Skills:
- • Students will have advanced skills in the following areas: problem solving, synthesis of data, critical and analytical thinking, report writing
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- • Lectures demonstrate what is required to be learned and the application of the theory to practical examples. • Computer practicals consolidate the studied material and enhance practical understanding. • Assignments for self-study develop problem-solving skills and enable students to test and develop their knowledge and understanding. • Formative assessments provide feedback to guide students in the correct development of their knowledge and skills in preparation for the summative assessment. • The written project report assesses the ability to implement the concepts introduced in the module using statistical software, to apply them in the analysis of a realistic problem, and to report scientific outputs in a clear and structured way. • The end-of-year examination assesses the knowledge acquired and the ability to solve predictable and unpredictable problems.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| Lectures | 21 | Two per week in weeks 1-10, one in week 21 | 1 hour | 21 | |
| Computer Practicals | 4 | Weeks 2, 4, 6, 8 | 1 hour | 4 | ■ |
| Preparation and reading | 75 | ||||
| Total | 100 |
Summative Assessment
| Component: Examination | Component Weighting: 80% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Written examination | 2 hours | 100% | |
| Component: Coursework | Component Weighting: 20% | ||
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Mini project report | 100% | ||
Formative Assessment:
Three written or electronic assignments to be assessed and returned. Other assignments are set for self-study and complete solutions are made available to students.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University