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2020/21 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 2020/21

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 and R) 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 and in R, the logic and syntax required for effective computer programming, programming expertise in Python and R, how to manipulate and plot environmental data sets, best practise in layout and structure of Python and R 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
• R
- A range of analytical and statistical tools including –
- 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

Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information

Delivery typeNumberLength hoursStudent hours
Workshop103.0030.00
Drop-in Session51.005.00
Lecture101.0010.00
Practical101.0010.00
Independent online learning hours15.00
Private study hours145.00
Total Contact hours55.00
Total hours (100hr per 10 credits)215.00

Private study

Approximately 1.5-hours per programming workshop is allocated to the completion of additional online computer programming tutorials (‘Independent Online Learning’; suggestions from internet rather than course-specific tutorials developed in Leeds).
It is recommended that the remaining hours of private study are used to complete outstanding tasks on non-assessed weekly computer worksheets/exercises (which will be primarily completed in class, where assistance from demonstrators and module teaching staff is available) and finalisation of the assessed computer exercises and project report.

Opportunities for Formative Feedback

Python: 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. Detailed written feedback for each individual will be provided on the first assessment (Worksheet 1), which is formative. Written cohort-level feedback will be provided on Worksheet 2 and the Report.
R stats: Students can ask questions and discuss examples with staff and demonstrators each week. There will also be an in-class exercise that will be marked by demonstrators and returned to the students as formative feedback.

Methods of assessment

Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information


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%). [Summative]100.00
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: 29/03/2021 10:51:06

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