Module Code: CMT212 Coursework Assessment Pro-forma

Posted 2021-02-13 whltay

Coursework Assessment Pro-forma
Module Code: CMT212
Module Title: Visual Communication and Information Design
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
Fail Pass Merit Distinction
Dataset
selection and
analysis
(20%)
No real data used,
or dataset ‘fake’
No/basic analysis of
data
Real-world data
selected
Cursory high-level
analysis of data
Real-world data
selected
Data analysed in
detail
Appropriate
statistical methods
used to draw
conclusions
Multiple real-world
datasets on similar
theme selected
Appropriate
statistical methods
used to
compare/relate
datasets
Visualisation
and Data
Presentation
(60%)
None/poor
visualisation of data
Poor data
presentation
No story conveyed
to user,
story/findings
unclear
Data visualised
appropriately
Message/story clear
to end user
Multiple appropriate
visualisations
End user able to
explore/interpret
data and affect
display
Message/story clear
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%)
No report/report
lacking in content
Little to no
evaluation
Analysis and
visualisation
process described
well.
Some effort at
evaluation
Analysis and
visualisation
process well
documented.
Reasonable
evaluation
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.

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