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2024/25 Taught Postgraduate Module Catalogue

SOEE5710M Advanced Data Analysis and Visualisation for Environmental Applications

15 creditsClass Size: 20

Module manager: Cathryn Birch

Taught: Semesters 1 & 2 (Sep to Jun) View Timetable

Year running 2024/25

This module is mutually exclusive with

SOEE2710Data Analysis and Visualisation for Environmental Applicatio
SOEE2810Data Analysis and Visualisation
SOEE2931Advanced Skills for Geoscientists

This module is not approved as an Elective

Module summary

This module is designed to teach you the computer programming skills required to analyse and plot environmental data sets, beyond what could be done using software such as Excel. The course begins with an introduction to the UNIX computer system and the basic principles of computer programming. Programming experience in the aspects of the Python language necessary for data manipulation and visualisation is developed through the course of the module. It is intended that the module will provide the skills required to write your own computer programs and edit the code of others to enable you to pursue your own research, such as for your Masters thesis.


On completion of this module students will be able to:
1. Navigate a UNIX environment
2. Design and implement medium-complexity computer programs in Python to read in, manipulate, compare and plot a range of environment-related data sets
3. Make a critical assessment of a computer program
4. Understand, edit and run more complex code written by others
5. Design code for efficiency

Learning outcomes
Computer literacy on a Linux terminal
Programming expertise in Python
Logic and syntax required for effective computer programming
How to read in, manipulate and output large environmental data sets in various formats
Ability to visualise environmental data sets through various plot types
Familiarity with Python packages relevant to environmental research
How to diagnose and fix errors in code
Best practise in layout and structure of programming scripts
Ability to understand, run and edit code written by others
Programming-related research skills

Skills outcomes
Computer literacy on Linux operating systems, the logic and syntax required for effective computer programming, programming expertise in Python, how to manipulate and plot environmental data sets, best practise in layout and structure of Python scripts.


- file-system navigation, basic text editor and file management
- reading large files of various data types (text files, netcdf)
- matrix manipulation (e.g. averaging, linking data sets)
- conditional statements and loops
- working with incomplete data sets
- time stamps
- data structures
- data visualisation and plotting (line, scatter, vector and contour plots, map projections, colour scales, adding multiple data types to same axes)
- writing scripts and functions
- formatting complex data sets for database cataloguing
- structured programming and debugging
- programming for efficiency with large data sets

Teaching methods

Delivery typeNumberLength hoursStudent hours
Computer Simulated Practical Techniques91.5013.50
Computer Class102.0020.00
In Course Assessment14.004.00
Independent online learning hours61.50
Private study hours51.00
Total Contact hours37.50
Total hours (100hr per 10 credits)150.00

Private study

Completion of outstanding tasks on non-assessed weekly computer worksheets, which students will begin in class, where assistance from demonstrators is available. Completion of the coursework assignment following the coursework workshop sessions.

Opportunities for Formative Feedback

Students will be able to ask questions and discuss examples with staff during the live-coding sessions each week. They will receive informal feedback on debugging codes, coding style and their responses to the non-assessed worksheets every week during the computer practical classes.
Formal written feedback will be provided for the assessed coursework and in-class assessment.

Methods of assessment

Assessment typeNotes% of formal assessment
Computer ExerciseInvestigation of the climate during the Last Glacial Maximum using climate model data, through guided programming tasks. Ability to write functional code (40%), code in a good style that produces quality plots (30%) and interpret the results (30%) are assessed.60.00
Computer Exercise4-hour time limited in-class assessed programming exercise in a computer cluster.40.00
Total percentage (Assessment Coursework)100.00

The resit is a single, assessed programming exercise.

Reading list

There is no reading list for this module

Last updated: 25/06/2024


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