Module Catalogue 2026/27

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

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.

Intended Knowledge Outcomes

PC1 Knowledge Application – Interpret detailed biological evidence and concepts and apply this knowledge to critically evaluate key issues in the discipline including its applied contexts. Students will apply their biological knowledge and skills developed as part of this module to critically evaluate an applied multi-disciplinary real-world problem (Assessed).

Intended Skill Outcomes

PC2 Information Literacy – Critically analyse and evaluate scientific literature to provide an answer to a question with an uncertain answer. Students will identify and critically evaluate scientific literature in order to address the industry-led problem solving group activity. Students taking topic area on systematic reviews and meta-analyse will additionally be introduced to techniques and professionally recognised workflows for undertaking systematic reviews of the literature (Introduced, Developed and Assessed).

PC4 Data Literacy – Find, evaluate, visualise, analyse and interpret data appropriately in a moderately structured environment. Apply data management techniques appropriate to your discipline in a moderately structured environment.
Students will find, evaluate, visualise and analyse data using statistical tests (Introduced, Developed and Assessed).

PC5 Communication - Comprehend and adopt appropriate academic language and conventions in order to communicate more complex scientific concepts clearly, concisely and correctly. Students will demonstrate their ability to do this through a problem-based activity (Developed and Assessed).

PC6 Digital Literacy – Identify and utilise different types of digital technology appropriate to the discipline to communicate scientific concepts clearly, concisely, and correctly in a variety of digitally enhanced formats. Students will be introduced to important digital technologies used regularly in scientific research and industry in the form of R Studio and QGIS (Introduced, Developed and Assessed (R Studio)).

PC8 Collaboration – Apply professional and digital collaboration skills in various settings to advance shared endeavours.
Implement principles of effective teamwork, acknowledging both personal contributions and the significance of others within the team. Via the industry-led problem-based exercise students will need to apply professional collaboration skills by working effectively in a multi-disciplinary team to reach a common outcome (Developed and Assessed).

PC9 Professional Skills & Career Management – Undertake experiential learning to apply both theoretical knowledge and practical skills in a professional context. Deepen understanding of your subject area in applied settings and develop a mindset that combines problem identification and solving, innovation, creativity, communication, and practical action.
Through the industry-led problem-based exercise students will apply theoretical knowledge and digital skills to tackle a real-world scenario in a multi-disciplinary setting (Developed and Assessed).

PC10 Integrated Problem Solving – Demonstrate and show resilience in applying problem solving approaches to complex questions using evidence to support the decisions, recognising that there may be more than one solution.
The skills developed as part of this module will be brought together to address a complex industry-led real-world problem. Given the multi-disciplinary nature of this problem students will have to work together an integrated way to provide the solution (Developed and Assessed).

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 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.

General Notes

N/A