Module Information

Module Identifier
MAM5220
Module Title
Statistical Techniques for Computational Biology
Academic Year
2017/2018
Co-ordinator
Semester
Semester 2
Pre-Requisite
Reading List
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.

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

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