APS 405 Advanced Biological Analysis

Level 4
Semester 2
Credits 10
Teaching Staff Dr Andrew Beckerman, Dr Dylan Childs, Dr Jon Slate
Co-ordinator Dr Andrew Beckerman

Description

This module provides training in advanced data management and statistical analysis using the open source statistical programme, R. Through a series of practicals, the student will build on their L1 and L2 training in R to learn how to manage complex data and implement a set of set of advanced statistical tests that include advanced linear models (ANCOVA), generalised linear models, mixed effects models and multivariate tools. Advanced graphing and visualisation is also emphasised throughout the module.

Semester 1 uses self-directed learning (video, web material and practical exercises) and regular, practical help sessions to revise/develop core skills in data management, visualisations and basic statistics. Students will learn about using R with RStudio, data management and manipulation with dplyr and ggplot2 packages and an (re)introduction to t-tests, contingency table analyses, linear regression and 1-way ANOVA.

Semester 2 is based on focused 3-day, 12-hour block practical sessions to introduce more advanced topics, including ANOVA and ANCOVA, generalised linear models, mixed models, spatial data analysis, phylogenetic contrasts and next generation sequencing modelling.

Aims

• Develop student skills in data management and visualisation.
• Develop student skills in reproducible statistical analysis,
• Develop student skills in interpreting scientific data with appropriate statistical tools.
• Provide advanced numeracy training via the use of open-source, freely available and cross-platform software.

Learning Outcome

By the end of the module, the student will be able to:
• Manage their data;
• Apply critical and analytical skills to the analysis of data and production of graphs and figures;
• Analyse, synthesise and summarise information critically;
• Prepare, process, interpret and present data using appropriate quantitative techniques in a statistical programme;
• Develop skills necessary for self-managed and lifelong learning in jobs requiring data management, analysis and report writing.

Student Contact Time: The teaching will be in the form of 10 workshops that integrate lectures with complementary, self-directed practicals.

Assessment Method: Assessment of this module is centred on attendance of lectures, satisfactory completion of the practical component of the module and an exam consisting of short answer questions and an analysis.

Feedback: The students received feedback and contact during 10, 2-3 hour intensive practicals.


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