Module Information
Module Identifier
MAM5220
Module Title
Statistical Techniques for Computational Biology
Academic Year
2017/2018
Co-ordinator
Semester
Semester 2
Pre-Requisite
Other Staff
Course Delivery
| Delivery Type | Delivery length / details |
|---|---|
| Seminar | 11 x 1 Hour Seminars |
| Lecture | 8 x 2 Hour Lectures |
| Practical | 9 x 2 Hour Practicals |
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | Three practical portfolios 3 x 30% Consultancy excercises 10% | 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 in Computational Biology
Apply the more advanced capabilities of R to analyze complex Biological data
Critically evaluate the application of specific statistical techniques to specific research problems in Computational Biology
Interpret and report effectively the results of statistical analyses
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 in depth three of these.
Students will also gain experience of Statistical consultancy.
The more advanced capabilities of R will be explored and mastered by applying statistical techniques to problems in Computational Biology.
Students are introduced to a variety of new techniques and applications, and proceed to study in depth three of these.
Students will also gain experience of Statistical consultancy.
The more advanced capabilities of R will be explored and mastered by applying statistical techniques to problems in Computational Biology.
Content
1. Introduction to a number of advanced topics such as:
Multifactorial ANOVA
Repeated Measures
Split Plots
Cross-over experimental design
MANOVA
Multivariate and discriminant methods
Time series
Epidemiology
Generalized linear models
Binomial and Poisson regression
Bootstrapping
2. Detailed study of three of the topics in (1).
3. Examples of statistical consultancy
Multifactorial ANOVA
Repeated Measures
Split Plots
Cross-over experimental design
MANOVA
Multivariate and discriminant methods
Time series
Epidemiology
Generalized linear models
Binomial and Poisson regression
Bootstrapping
2. Detailed study of three of the topics in (1).
3. Examples of statistical consultancy
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