2022/23 Undergraduate Module Catalogue
SOEE2810 Data Analysis and Visualisation
20 creditsClass Size: 100
Module manager: Ben Mills
Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2022/23
Module replacesPartly replaces aspects of SOEE2700
This module is not approved as a discovery module
Module summaryThis 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.
ObjectivesThis 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.
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
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.
- 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
|Delivery type||Number||Length hours||Student hours|
|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 studyApproximately 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 FeedbackPython programming: 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.
Statistics: Students can ask questions and discuss examples with staff and demonstrators each week. In addition to the weekly lectures and computer classes, worksheets will be provided every other week for students to test their understanding. These can be discussed during the weekly computer classes and sample solutions will be provided at the end of every 2-week period.
Methods of assessment
|Assessment type||Notes||% of formal assessment|
|Report||Presentation 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]||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 listThere is no reading list for this module
Last updated: 26/05/2022 14:15:15
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