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Module ONS-1001:
Environmental data & analysis

Module Facts

Run by School of Ocean Sciences

20 Credits or 10 ECTS Credits

Semester 1 & 2

Organiser: Dr Jennifer Shepperson

Overall aims and purpose

To develop in students an understanding of the fundamental skills required by natural scientists to measure, map and quantify the environment in which they work. This includes the key concepts of the scientific method, experimental design, mapping, basic mathematical manipulations, data visualisation, and statistical analysis and hypothesis testing. Students should become confident in the use of appropriate computer software (Excel, R and/or Matlab) to analyse environmental data.

Course content

This module provides students with the fundamental skills required by natural scientists to answer scientific questions with environmental data.

Theory is put into practice through computer sessions, to apply a range of data analysis techniques to environmental data. In the first semester students are introduced to the scientific method, how to describe samples numerically and graphically, and how to test hypotheses statistically to identify differences and relationships between variables.

In the second semester, as well as additional statistical theory and practical sessions, skills are applied in a subject specific project. In this project, students conduct a scientific investigation, including collecting and analysing data; the results of this data analysis are communicated in the style of a scientific report.

Assessment Criteria


A threshold student should have a basic knowledge of the scientific method, have a basic ability to quantitatively manipulate datasets using a range of fundamental mathematical tools using appropriate computer software, and be able to use and interpret statistical tests.


A good student should competent at quantitatively manipulating datasets using a range of fundamental mathematical tools, have a good ability to interpret datasets and be able to confidently use and interpret statistical tests using appropriate computer software.


An excellent student should have a sophisticated knowledge of quantitatively manipulating datasets using a range of fundamental mathematical tools, have an advanced ability to interpret datasets and be highly skilled in the use and interpretation of statistical tests using appropriate computer software.

Learning outcomes

  1. Identify and explain the most appropriate strategy for analysing data, including recognising the assumptions and limitations of each approach.

  2. Produce simple maps to communicate scientific methods or findings.

  3. Communicate the results of scientific investigations and data analysis in the style of a scientific report.

  4. Manipulate, summarise, visualise, and analyse data using Excel and R computer software.

  5. Numerically and graphically summarise data to communicate key scientific results.

  6. Plan and conduct a simple scientific investigation with appropriate regard to design and analysis issues.

  7. Explain hypothetico-deductive reasoning, and formulate testable hypotheses.

  8. Apply inferential statistical tests to data to test a hypothesis, to investigate differences or relationships, interpreting the results in the context of the subject specific research question.

  9. Report numerical values to an appropriate level of precision, and use appropriate units of measurement.

Assessment Methods

Type Name Description Weight
REPORT Data collection and analysis scientific report

Scientific report communicating the results of a subject specific project . All semester 2 activities are focused on developing the skills required to successfully complete this report.

CLASS TEST T1 - Scientific method and describing samples midterm

Short online Blackboard test to examine the understanding of the scientific method, distributions, ways to describe a single sample, and basic numerical concepts including units and significant figures

CLASS TEST T2 - Data analysis and hypothesis testing

Online Blackboard test to examine understanding of how to analyse data using Excel and R, including using hypothesis testing to identify significant differences or relationships between variables.


Teaching and Learning Strategy


Directed learning materials, including video lectures and short tasks.

Private study

Online computer practical resources. One information pack per week, including computer practical handouts and tutorial videos


Online Q&A sessions, where further explanation and clarification are provided about the weeks lecture and practical material.

Study group

Independent study groups to facilitate peer learning.


Fieldwork activity to collect data to map and measure the environment, and collect scientific data

Private study

Approx. 5 hours per week each semester. Private self-study utilising directed reading materials and reinforcement of lecture, workshop, and project materials.


Timetabled support for the online computer practicals, with lecturer and demonstrators available to assist.

Individual Project

Supervised data collection, analysis and write-up of a subject specific project.


Transferable skills

  • Literacy - Proficiency in reading and writing through a variety of media
  • Numeracy - Proficiency in using numbers at appropriate levels of accuracy
  • Computer Literacy - Proficiency in using a varied range of computer software
  • Self-Management - Able to work unsupervised in an efficient, punctual and structured manner. To examine the outcomes of tasks and events, and judge levels of quality and importance
  • Exploring - Able to investigate, research and consider alternatives
  • Information retrieval - Able to access different and multiple sources of information
  • Inter-personal - Able to question, actively listen, examine given answers and interact sensitevely with others
  • Critical analysis & Problem Solving - Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.
  • Safety-Consciousness - Having an awareness of your immediate environment, and confidence in adhering to health and safety regulations
  • Presentation - Able to clearly present information and explanations to an audience. Through the written or oral mode of communication accurately and concisely.
  • Teamwork - Able to constructively cooperate with others on a common task, and/or be part of a day-to-day working team
  • Argument - Able to put forward, debate and justify an opinion or a course of action, with an individual or in a wider group setting
  • Self-awareness & Reflectivity - Having an awareness of your own strengths, weaknesses, aims and objectives. Able to regularly review, evaluate and reflect upon the performance of yourself and others


Pre- and Co-requisite Modules

Courses including this module

Compulsory in courses: