Cardiff School of Computer Science and Informatics Coursework Assessment Pro-forma
Module Code: CMT212
Module Title: Visual Communication and Information Design Lecturer: Dr Martin Chorley
Assessment Title: Data Analysis and Visualisation Creation Assessment Number: 2
Date Set: 4th March 2019
Submission Date and Time: 7th May 2019 at 9:30am.
Return Date: 4th June 2019
This assignment is worth 70% of the total marks available for this module. The penalty for late or non-submission is an award of zero marks.
Your submission must include the official Coursework Submission Cover sheet, which can be found here:
https://docs.cs.cf.ac.uk/downloads/coursework/Coversheet.pdf
Submission Instructions
All submission should be via Learning Central. The current electronic coursework submission policy can be found at: http://www.cs.cf.ac.uk/currentstudents/ElectronicCourseworkSubmissionPolicy.pdf
Your submission should consist of a collection of code/documents used to analyse and visualise your selected data, alongside a report detailing the process used.
Description
Type
Name
Cover sheet
Compulsory
One PDF (.pdf) file
[student number].pdf
Data Analysis and Visualisation
Compulsory
One zip archive (.zip) containing all code used to extract, analyse and visualise data
DAV_[student number].zip
Process Report
Compulsory
One PDF (.pdf) or Word file (.doc or .docx)
PR_[student number].pdf/doc/docx
Any deviation from the submission instructions above (including the number and types of files submitted) may result in a mark of zero for the assessment or question part.
Assignment
You are asked to carry out an analysis of a dataset(s) and to present your findings in the form of a report and visualisation(s), along with a record of your analysis.
You should find one or more freely available dataset(s) on any topic, from a reliable source. You may wish to choose something from data.gov.uk or ons.gov.uk for example.
You should then carry out an analysis of this data to determine what the data tells you about its particular topic. You may wish to use different statistical methods to describe the data set, or to infer what the data tells us in a wider context. You should then visualise your data in a way that allows a user to understand the data and what the data shows about its topic. You can use any language or tool you like to carry out both the analysis and the visualisation, but all code used must be submitted as part of the coursework. For example, you may wish to extract, transform and analyse the data using Python, and then create visualisations using d3.js.
You should create a report of your process that includes a description of the data and your visualisation(s). Alongside this you should document your analysis methods and the procedure used to create your visualisation. This record should include a commentary of the code used to extract/transform/analyse data and show the development of the resulting visualisation(s), including any prototype or rejected visualisations/analyses. It should also include a reflective evaluation of your finished analysis.
Important! It is expected that each student will choose a different dataset. Once you have chosen your dataset(s) for analysis, you should complete the form at http://bit.ly/cmt212- 1819-cw2 with your selection to confirm it is a unique choice. Dataset allocation will be done on a first-come, first-served basis, so do not delay, as another student may ¡®claim¡¯ the dataset first! Data selection should be completed by 18th March at 5PM. Any data redistribution as part of your submission must abide by the licence under which the data was obtained.
Learning Outcomes Assessed
3. Examine and explore data to find the best way it can be visually represented 4. Apply statistical methods to data
5. Access web APIs and data sources, retrieve and manipulate data
6. Create static, animated and interactive visualisations of data
Criteria for assessment
Credit will be awarded against the following criteria.
Component & Contribution
Dataset selection and analysis (20%)
Fail
No real data used, or dataset ¡®fake¡¯ No/basic analysis of data
None/poor visualisation of data Poor data presentation
No story conveyed to user, story/findings unclear
No report/report lacking in content Little to no evaluation
Pass
Real-world data selected
Cursory high-level analysis of data
Data visualised appropriately Message/story clear to end user
Analysis and visualisation process described well.
Some effort at evaluation
Merit
Real-world data selected
Data analysed in detail
Appropriate statistical methods used to draw conclusions
Multiple appropriate visualisations
End user able to explore/interpret data and affect display Message/story clear
Analysis and visualisation process well documented. Reasonable evaluation
Distinction
Multiple real-world datasets on similar theme selected Appropriate statistical methods used to compare/relate datasets
Visualisation and Data Presentation (60%)
Multiple appropriate visualisations with interaction and/or appropriate animation
End user able to explore/interpret data and affect display Message/story clear
Process Report (20%)
Analysis and visualisation process thoroughly documented. Insightful evaluation
Feedback and suggestion for future learning
Feedback on your coursework will address the above criteria. Feedback and marks will be returned on 4th June 2019 via email. Additional group feedback will be provided online via video.