Module Information
Module Identifier
MAM5220
Module Title
Statistical Techniques for Computational Scientists
Academic Year
2026/2027
Co-ordinator
Semester
Semester 2
Pre-Requisite
Reading List
Other Staff
Course Delivery
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | Three practical portfolios 3 x 25% Consultancy exercises 25% (9% for presentation, 16% for essay) | 100% |
| Supplementary Assessment | Resubmission of failed components | 100% |
Learning Outcomes
On completion of this module, students should be able to.
Select and apply advanced statistical methods to research problems
Apply the more advanced capabilities of R to analyze complex data
Select and apply advanced statistical methods to research problems
Interpret and report effectively the results of statistical analyses
Brief description
The more advanced capabilities of R will be explored and mastered by applying statistical techniques to problems in Computational Biology.
Aims
This module will allow students to master the more advanced capabilities of R by using them to apply a variety of statistical techniques to problems in Computational Biology. Students are introduced to a variety of new techniques and applications, and proceed to study three of these in depth.
Students will also gain experience of Statistical consultancy.
Students will also gain experience of Statistical consultancy.
Content
1. Introduction to a number of advanced topics such as:
MANOVA
Principal Component Analysis
Time series
Epidemiology
Generalised linear models
Transcriptomics
MANOVA
Principal Component Analysis
Time series
Epidemiology
Generalised linear models
Transcriptomics
Module Skills
| Skills Type | Skills details |
|---|---|
| Application of Number | Inherent in the study of statistics and statistical methods |
| Communication | Consultancy exercises |
| Improving own Learning and Performance | Awareness of advanced techniques and detailed study of some of these |
| Information Technology | Mastery of the advanced capabilities of R |
| Personal Development and Career planning | Experience of statistical consultancy |
| Problem solving | Identifying and using statistical techniques to solve problems in Computational Biology |
| Research skills | Experimental design |
| Subject Specific Skills | Expertise in advanced analysis techniques |
| Team work | Joint work in consultancy |
Notes
This module is at CQFW Level 7