Postgraduate Biology 2017-2018

This course is uniquely tailored for biologists and will provide students with the required background and skills relevant to modern ecology and biology. The unique format of example-led lectures and real-world based practicals will provide you with a foundation to become confident and proficient in dealing with real data. Throughout this course, we will introduce you to using the programming language R (an industry standard) to implement modern statistical modelling techniques. You will use the flexible linear and generalised modelling framework to analyse biological data.

This highly regarded course will take your understanding of statistics to the next level and provide you with the skills and confidence to analyse your complex biological data. Through a combination of lectures, computer based practicals and group work you will gain an understanding of how to deal with pervasive issues in the analysis of real world biological data such as heterogeneity of variance and spatial and temporal non-independence. Hands on computer tutorials will allow you to apply statistical models using modern statistical software (R) to real data, collected by researchers to answer real biological questions.

Enables students to increase and enhance their understanding of atmospheric, aquatic and terrestrial environmental pollution issues;

Addresses the management, monitoring and control of environmental pollutants;

Allows students to engagement with stake holders from a number of different sectors;

Uses both qualitative and quantitative data to understand the impact of environmental pollutants.

This course is one of the few postgraduate courses in Europe to provide an introduction to Bayesian inference for biologists, which is increasingly used in advanced quantitative research. A combination of lectures and personal research will provide you with the core concepts necessary to understand recent research in your field and apply Bayesian approaches to your own research. Hands-on computer tutorials will also allow you to implement statistical models in a Bayesian context and provide you with the essential skills for taking it further.

The aim of the course is to explore a topic within ecology, nature conservation or environmental sustainability through reading an influential book of own choice, synthesise the information and present this synthesis in an oral format to peers. Upon completion of the course students will be able to: i) explain in detail a topic of study in ecology, conservation or environmental sustainability to peers; ii) demonstrate synthetic knowledge by answering questions about the topic in an oral examination; and iii) discuss the relevance of their topic to broader issues in ecology, nature conservation and environmental sustainability.

Introduction to techniques used in environmental and ecological modelling.

Main course materials are in three parts; model development, model evaluation and model communication.

Hands-on practice is gained in model development and evaluation in two 4 hour computer classes.

Course assessment is based on the write up of the two computer sessions (30% each), and an exercise on model communication (20%). The last 20% of the course marks are given for a reflective log completed on the course materials.

The course provides students with the key skills required for environmental and ecological modelling.