2026/7 – ENV-4017B Data and Research Skills B

Spring Semester, Level 4 module, Credits: UCU 20

Organiser: Dr Amii Harwood

Assessment Type: Examination with Coursework or Project

Timetable Slot: D 

Data and Research Skills B builds directly upon the foundational skills developed in Data and Research Skills A, advancing students' capabilities in statistical analysis, research methods, and data handling. This module teaches how to test hypotheses (when required) and draw meaningful conclusions from environmental and geographical data through more sophisticated analytical techniques. The first half of the module develops essential quantitative and analytical skills using real environmental datasets. Students learn to design robust research studies, work with statistical concepts - from t-tests to regression analysis - and apply mathematical approaches to understanding environmental change. We explore spatial patterns through techniques like spatial autocorrelation, examine temporal trends through time series analysis, and develop practical approaches to calculating rates of change in environmental systems. Throughout these sessions, students gain hands-on experience with both mathematical and statistical tools, from interpreting confidence intervals to analysing gradients in meteorological data. Following this, most students develop qualitative research skills including question and survey design, while geophysics students undertake focused work consolidating ‘missed’ critical thinking from Data and Research Skills A. All students then participate in a residential field course at Slapton Field Centre during the Easter break, applying their research and analytical skills to real environmental challenges. After Easter, students choose between three specialist pathways: further statistics (including non-parametric statistics and regression techniques), or an introduction to environmental programming with either R or Python. The R pathway, will provide an introduction to the scripting language with a focus on spatial analysis and mapping. In the Python pathway students learn to automate data analysis and create sophisticated visualisations of environmental data. Both programming pathways will incorporate contemporary tools and techniques, including emerging AI technologies where appropriate. Throughout the module, we emphasize hands-on learning through practical workshops using industry-standard tools and techniques. Students develop professional skills valued by employers and research organizations, emerging confident in research design, data analysis, and technical communication.