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2019/20 Undergraduate Module Catalogue

SOEE2710 Data Analysis and Visualisation for Environmental Applications

10 creditsClass Size: 35

Module manager: Dr Alex Rap
Email: a.rap@leeds.ac.uk

Taught: Semester 1 View Timetable

Year running 2019/20

Module replaces

Replaces SOEE2240 for MEnv, BSc Meteorology and Climate Science, MEnv, BSc Environmental Science and MRes Climate and Atmospheric Science, however, SOEE2240 will continue (currently oversubscribed)

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 typeNumberLength hoursStudent hours
Workshop103.0030.00
Independent online learning hours35.00
Private study hours35.00
Total Contact hours30.00
Total hours (100hr per 10 credits)100.00

Private study

Completion of additional online computer programming tutorials (suggestions from internet rather than course-specific tutorials developed in Leeds).
Completion of outstanding tasks on non-assessed weekly computer worksheets. These will be mainly be completed in class, where assistance from demonstrators and the module leader is available.
Finalisation of the two assessed computer exercises and the project report.

Opportunities for Formative Feedback

Students will be able to ask questions and discuss examples with staff and demonstrators 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 two assessed worksheets and the project report.

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
ReportPresentation and interpretation of results from mini research project (guided by programming tasks). Report of max. 1500 words (30%), quality of computer code (35 %) and quality of data visualisation (35%).60.00
Computer ExerciseWorksheet 1, where computer programming tasks are completed.0.00
Computer ExerciseWorksheet 2, where computer programming tasks are completed.40.00
Total percentage (Assessment Coursework)100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

There is no reading list for this module

Last updated: 25/07/2019

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