• Offered for Year: 2026/27

• Module Leader(s): Professor Pip Moore
• Lecturer: Professor Clare Fitzsimmons, Dr William Reid, Dr Roy Sanderson, Dr James Stach

• Owning School: Natural and Environmental Sciences
• Teaching Location: Newcastle City Campus

Semesters

Your programme is made up of credits, the total differs on programme to programme.

Aims

To introduce and practise the development of hypotheses, appropriate experimental design, robust data manipulation and analysis, appropriate statistical testing and interpretation.
This module aims to develop core skills and knowledge that employers in the biosciences have identified as the most sought after. These include experimental design, data analysis and visualisation, geospatial (GIS), systematic reviews, team work, problem solving and communication.

All students will be introduced to experimental design and statistics delivered through a combination of taught material and linked practical exercises aimed at developing the problem-solving and statistical skills required by research biologists. It will introduce the concepts of hypothesis driven research, proper experimental design, data manipulation and statistical testing. Students will learn analyses using R and interactive websites. Students will then have the option of developing their skills in one or more areas including Geographical Information Systems (GIS), systematic reviews and meta-analyses or advanced data visualisation. The module will culminate in students working in multi-disciplinary groups to provide innovative solutions to an industry-led real-world problem drawing on the wider skills and knowledge developed in this module. This multidisciplinary working will allow students to explore, discuss and understand the role of people, policies and practices from different disciplinary perspectives, and begin to draw these perspectives together to identify interdisciplinary solutions to problems.

Outline Of Syllabus

Data analysis/Statistics:
Lectures covering key points to be developed, practised and assessed through the practical sessions with online interactive websites and short, user-friendly videos to introduce concepts on:
-Understanding different types of data, response and explanatory variables.
-How to visualise and summarise data.
-Good practice in experimental design
-Linear models as a general approach to analysing univariate data
-Generalised linear models to analyse data with non-normal distributions
-Further regression methods: model checking; quadratic regression; multiple regression.
-Review: matching statistical analyses to hypotheses and data.

GIS:
Lectures will introduce students to GIS and working with spatial data with practical sessions introducing students to the software QGIS where students will put in to practice skill in:
Projections, datums and understanding spatial data
Introduction to vectors and rasters
Data structure
Points, lines and polygons
Mapping

Systematic reviews and meta-analysis:
Through lectures and workshops students will be introduced to the concept of systematic reviews and meta-analysis, in particular they will cover:
Systematic searches of the literature
The difference between a quantitative review and a meta-analysis
The workflow followed for a systematic review
PRISMA guidelines
Formulas to calculate hedge’s g

Problem-based exercise:
Lectures will introduce students to a real-world environmental challenge as pitched by an industry partner. Students will then work in groups to provide a solution to this problem

Advanced data visualisation:
Students will further develop skills introduced in the data analysis part of this course to develop publication quality visual outputs. These skills will be developed via workshops.

Teaching Methods

Teaching Activities

Teaching Rationale And Relationship

Lectures will introduce students to the different skills being developed in the module and those focused on the problem-based activity will help frame the problem and provide additional background and the necessary context to tackle the problem based exercise. The problem-based exercise provides students with the opportunity to work on a real-world problem from a peer-focused range of different disciplinary perspectives.
Practicals allow students to put into practice the skills introduced via lectures.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment

Formative Assessments

Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.

Assessment Rationale And Relationship

The formative and summative computer assessments examine students' understanding of experimental design and statistics and ability to think in a logical manner including identifying appropriate analyses.

The presentation will provide students with the opportunity to develop their oral communication skills while showcasing their knowledge, understanding and solution to the problem based exercise. The group element enables students to work in an multidisciplinary environment to consider the problem from different viewpoints.

Study abroad students may request to take their exam before the semester 1 exam period, in which case the format of the paper may differ from that shown in the MOF. Study abroad students should contact the school to discuss this.

Reading Lists

• NES2504's Reading List

Timetable

• Timetable Website: www.ncl.ac.uk/timetable/
• NES2504's Timetable