2023/24 Undergraduate Module Catalogue

SOEE2810 Data Analysis and Visualisation

20 creditsClass Size: 100

Module manager: Ben Mills
Email: b.mills@leeds.ac.uk

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

Year running 2023/24

This module is mutually exclusive with

SOEE2710	Data Analysis and Visualisation for Environmental Applicatio
SOEE2931	Advanced Skills for Geoscientists
SOEE5710M	Advanced Data Analysis and Visualisation for Environmental A

Module replaces

Partly replaces aspects of SOEE2700

This module is not approved as a discovery module

Module summary

This module aims to provide Environmental Science students with a core set of transferable computing and analytical skills to make them highly competitive in the pursuit of their subsequent careers and to facilitate their transition from Higher Education study to the workplace. Recognising that proficiency in computer programming is becoming increasingly necessary and valuable for data analysis (in particular for processing and visualising large/complex datasets), this module aims to provide a broad and solid foundation in this skill to act as a springboard for more advanced or specialist computing, both in research and the workplace. In this module, you will learn 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

This module provides key statistical and programming skills to equip Environmental Science students for the modern workplace. On completion of this module, students will:
- Be proficient in the use of computer programming (using Python) for undertaking a flexible range of tasks
- Students will learn how to how to analyse the data they collect and how to draw inferences.
- Students will have an opportunity to gain practical experience of how environmental data is analysed, interpreted and reported.

Learning outcomes
1. Practice skills in measurements, analysis, synthesis and integration of information, and in the application of related theoretical knowledge, where relevant.
2. Understand how to apply a range of statistical techniques for hypothesis testing with numerical data and social science survey data.
3. You will be able to perform simple operations on Linux systems (e.g. moving between and managing directories, text editing)
4. You will be able to design and execute efficient, simple computer programs (in Python) for reading, manipulating, analysing (including plotting) and outputting data
5. You will be able to diagnose and correct errors in code

Skills outcomes
The programming elements of the module will train students in 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

Programming
• UNIX
- file-system navigation, basic text editor and file management
• 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
- STATISTICAL ANALYSIS USING PYTHON
- Distributions and probability
- Hypothesis testing: T-tests and Chi-squared tests
- Analysis of variance
- Correlation and Regression
- Non-parametric statistics
- Treatment of data errors
- Basics of quantitative and qualitative error analysis

Teaching methods

Delivery type	Number	Length hours	Student hours
Computer Class	10	1.00	10.00
Computer Class	10	2.00	20.00
Lecture	1	1.00	1.00
Lecture	8	1.50	12.00
Lecture	10	1.00	10.00
Independent online learning hours			15.00
Private study hours			132.00
Total Contact hours			53.00
Total hours (100hr per 10 credits)			200.00

Private study

Completion of outstanding tasks on non-assessed weekly computer worksheets, where assistance from demonstrators is also available. Completion of the coursework assignment following the coursework workshop sessions.A

Opportunities for Formative Feedback

Students will be able to ask questions and discuss examples with staff during the practical sessions each week. They will receive informal feedback on debugging code, 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
Report	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.	50.00
In-course Assessment	An in-course test assessing statistics knowledge	50.00
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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