Environmental Data Handling and Presentation | Abertay University

Environmental Data Handling and Presentation


This module uses a case study approach to introduce students to the technology and methodology for acquiring, processing and presenting environmental data.


The aim of this Module is to provide the student with the analytical and computational skills for handling and presenting environmental data.

Learning Outcomes

By the end of this module the student should be able to:

1.  Acquire understanding of the main techniques and technologies for data collection and evaluate the challenges associated with this process.

2.  Demonstrate competency in the use of methods for summarizing and presenting environmental data and apply these methods using a range of appropriate software packages.

3.  Demonstrate competency in the use of methods for handling and visualizing data from imaging and sensing technologies.

Indicative Content

1 Introduction

Living in a data-rich world. The nature and challenges of environmental data.

2 Methods for data collection

Sources of data in environmental sciences, techniques and technologies, observation vs experimentation, sampling methods, crowdsourcing environmental data.

3 Data exploration and presentation

Type of data/type of variables - qualitative vs quantitative, descripting methods, summary statistics and graphical representation, finding patterns in data and formulation of research hypothesis.

4 Case studies

Application of analytical methods to monitor and assess air quality and pollution data.

5 Data handling and presentation software


Statement on Teaching, Learning and Assessment

This is a single trimester module, which will use problem-base learning to introduce students to the methodology and technology used for collection, handling and presentation of environmental data. A teaching approach based on case studies relevant to environmental sciences will be adopted to introduce the exploratory and visualization methods methods via a combination of interactive lectures and laboratory classes to provide practical implementation of the methods using appropriate statistical software. Learning will be assessed by two pieces of assessment: a first coursework assessing student learning on techniques and technologies for data collection, statistical exploration and presentation (counting for 50% of the overall module) and a second coursework (counting for 50% of the overall module) to assess student learning on methods for acquiring, handling and visualizing imaged data and data from sensory technologies.

Teaching and Learning Work Loads

Total 200
Lecture 12
Tutorial/Seminar 0
Supervised Practical Activity 28
Unsupervised Practical Activity 0
Assessment 40
Independent 120

Guidance notes

Credit Value – The total value of SCQF credits for the module. 20 credits are the equivalent of 10 ECTS credits. A full-time student should normally register for 60 SCQF credits per semester.


We make every effort to ensure that the information on our website is accurate but it is possible that some changes may occur prior to the academic year of entry. The modules listed in this catalogue are offered subject to availability during academic year 2018/19 , and may be subject to change for future years.