GV2ADNU-Analysing Data

Module Provider: Geography and Environmental Science
Number of credits: 10 [5 ECTS credits]
Level:5
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2021/2

Module Convenor: Dr Steve Robinson
Email: j.s.robinson@reading.ac.uk

Type of module:

Summary module description:

This module equips students with the necessary skills to assess and manipulate environmental data in order to assist in decision making. It is designed to provide a clear understanding of natural environmental processes and the impact of human activity on the environment (e.g. pollution and land management) through the analysis of data collected during practical investigations. Students will have the opportunity to collect and analyse their own data, as well as analyse real data sets provided from different environmental research studies.

Aims:

This module aims to develop skills in the management and analysis of environmental data. Outputs from a variety of practical investigations will be interpreted through the application of a range of standard and modern statistical methods. Microsoft Excel and other statistical software will be used.

Assessable learning outcomes:

By the end of the module students should have attained -

• Knowledge:
  
  
  
  • to explain the concepts, theories and techniques of statistical analysis
  
  
  • to use statistical skills to present data and apply relevant statistical tools to environmental problems.
  
  
  
  


• Critical thinking and problem-solving skills:
  
  
  
  • to explain the importance of sound research design and sample collection to ensure data are reliable and suitable for the statistical test required
  
  
  • to formulate a range of environmental hypotheses, design experiments and be able to choose proper statistical techniques to test these
  
  
  
  

to use the standard statistical techniques to interpret and analyze real environmental engineering problems.

Additional outcomes:

To become familiar with the use of Excel and/or other software in analyzing environmental data and problem solving.

Outline content:

The module covers the fundamental methods needed for environmental data analysis, including:

• Introduction to statistics - what are ‘statistics’ and why do we need them?


• Types of data; discrete and continuous, dependence and independence


• Frequency distributions (percentiles, polygons and curves)


• Simple measures used to describe data (e.g. measures of the ‘centre’, dispersion)

Graphical displays (e.g. scatterplots, boxplots, stem and leaf plots etc.)

• Describing distributions (IQR, variance, standard deviation, skewness, kurtosis)


• Probability models (special discrete distributions and normal distribution)


• Sampling distributions, estimation, confidence intervals,


• Hypothesis testing, t-test, ANOVA


• Testing for relationships using correlation and regression

Global context:

The statistical techniques represent a common toolkit for environmental issues worldwide.

Brief description of teaching and learning methods:

Teaching will be through interactive lectures (including group assignments and presentations) and computer laboratory practical sessions. Methods will be introduced and discussed in lectures and applied during the practical sessions, where students will collect and analyse their own data. The module includes blended teaching and learning methods, involving pre-reading/thinking, using PowerPoint slides, short video clips related to topics covered in the class, problem solving sessions and inte nsive open-ended assignments.

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures	28
Practicals classes and workshops	20
Guided independent study:	52
Ìý	Ìý	Ìý	Ìý
Total hours by term	100	0	0
Ìý	Ìý	Ìý	Ìý
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Written exam	55
Report	25
Oral assessment and presentation	10
Class test administered by School	10

Summative assessment- Examinations:

2 hours

Summative assessment- Coursework and in-class tests:

Group assignment (oral assessment and presentation): given to the students at the start of the semester; students are encouraged to use books, online literature, journals and factual data.Ìý

Laboratory Reports: Exercises will be given after each practical session whereby students should demonstrate their understanding of the topic.

Two in-class tests: one each during the first and second halves of the semester.

Formative assessment methods:

In-class spot quizzes will enable feedback and feed-forward on a topic by topic basis, and help the students to prepare for their summative assessments.

Penalties for late submission:

The Support Centres will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at:
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

A mark of 40% overall

Reassessment arrangements:

Re-examination in February

Additional Costs (specified where applicable):

1) Required text books:Ìý

2) Specialist equipment or materials:Ìý

3) Specialist clothing, footwear or headgear:Ìý

4) Printing and binding:Ìý

5) Computers and devices with a particular specification:Ìý

6) Travel, accommodation and subsistence:Ìý

Last updated: 2 December 2021