2023/24 Undergraduate Module Catalogue

SOEE2710 Data Analysis and Visualisation for Environmental Applications

10 creditsClass Size: 35

Module manager: Dr Cathryn Birch
Email: C.E.Birch@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2023/24

This module is mutually exclusive with

SOEE2810	Data Analysis and Visualisation
SOEE2931	Advanced Skills for Geoscientists
SOEE5710M	Advanced Data Analysis and Visualisation for Environmental A

This module is not approved as a discovery module

Module summary

This module is designed to teach you the basic 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 basic skills required to write the custom computer programs necessary for projects such as your final year dissertation.

Objectives

On completion of this module students will be able to:

1. Navigate a UNIX environment
2. Design and implement simple computer programs in Python to read in, manipulate and plot a range of environment-related data sets
3. Make a critical assessment of a computer program

Learning outcomes
Computer literacy on a linux operating terminal
Programming expertise in Python
Logic and syntax required for effective computer programming
How to read in, manipulate and output environmental data sets
Ability to visualise environmental data sets through simple plotting
How to diagnose and fix errors in code
Best practise in layout and structure of programming scripts

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.

Syllabus

1. LINUX
- file-system navigation, basic text editor and file management
2. PYTHON PROGRAMMING
- reading simple data types (e.g. text files)
- matrix manipulation (e.g. time and spatial means)
- conditional statements and loops
- data visualisation and plotting (line, scatter and contour plots)
- writing scripts and functions
- formatting simple output data
- structured programming and debugging

Teaching methods

Delivery type	Number	Length hours	Student hours
Computer Simulated Practical Techniques	8	1.50	12.00
Computer Class	10	2.00	20.00
Lecture	1	1.00	1.00
Independent online learning hours			32.00
Private study hours			35.00
Total Contact hours			33.00
Total hours (100hr per 10 credits)			100.00

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.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Computer Exercise	Investigation 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.	100.00
Total percentage (Assessment Coursework)		100.00

The resit is a single, assessed programming worksheet.

Reading list

There is no reading list for this module

Last updated: 07/12/2023 14:10:06

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