Module Code: CMT218

Module Title:  Data Visualisation

Lecturer: Dr Martin Chorley

Assessment Title: Data Visualisation Resit

Assessment Number: RESIT

Date Set: 17th July 2023

Submission Date and Time: by 7th August 2023 at 9:30am

Feedback return date: 6th September 2023

If you have been granted an extension for Extenuating Circumstances, then the   submission deadline and return date will be 1 week later than that stated above.

If you have been granted a deferral for Extenuating Circumstances, then you will be assessed in the summer resit period (assuming all other constraints are met).

This assignment is worth 100% of the total marks available for this module. If

coursework is submitted late (and where there are no extenuating circumstances):

1           If the assessment is submitted no later than 24 hours after the

deadline, the mark for the assessment will be capped at the minimum pass mark;

2           If the assessment is submitted more than 24 hours after the deadline, a mark of 0 will be given for the assessment.

Extensions to the coursework submission date can only be requested using the   Extenuating Circumstances procedure. Only students with approved extenuating  circumstances may use the extenuating circumstances submission deadline. Any coursework submitted after the initial submission deadline without *approved*   extenuating circumstances will be treated as late.

More information on the extenuating circumstances procedure can be found on the

Intranet:https://intranet.cardiff.ac.uk/students/study/exams-and-

assessment/extenuating-circumstances

By submitting this assignment you are accepting the terms of the following declaration:

I hereby declare that my submission (or my contribution to it in the case of group submissions) is all my own work, that it has not previously been submitted for assessment and that I have not knowingly allowed it to be copied by another student. I understand that deceiving or attempting to deceive examiners by passing off the work of another writer, as one’s own is plagiarism. I also understand that plagiarising another’s work or knowingly allowing another student to plagiarise from my work is against the University regulations and that doing so will result in loss of marks and possible disciplinary proceedings1.

Assignment

You are asked to carryout an analysis of a dataset and to present your findings in the form of a maximum of two (2) visualisations, (or a single (1) dashboard comprising a set of linked sub-visualisations) along with an evaluation of your work.

You should find one or more freely available dataset(s) on any topic, (with a small number of restrictions, see below) from a reliable source.

You should analyse this data to determine what the data tells you about its particular topic and should visualise this data in a way that allows your chosen audience to understand the data and what the data shows. You should create a maximum of two (2) visualisations of this data that efficiently and effectively convey the key message from your chosen data. It should be clear from these visualisations what the message from your data is.

You  can  use  any language or  tool you like to carry out both the analysis and the visualisation, with a few conditions/restrictions, as detailed below. All code used must be submitted as part of the coursework, along with the data required, and you must include enough  instructions/information  to  be  able  to  run  the  code  and   reproduce  the analysis/visualisations.

You should create a short (~4-5 page, ~2000 words) reflective evaluation of the success (or not!) of your completed visualisation(s). This evaluation should critically discuss the visualisations you have created, and should relate this discussion to the visualisation principles we have discussed in the module. It must *not* just be a description of what the visualisations show and what they tell us about the data. It should cover *why* your visualisations do a good job of communicating the information that you have found out about your  data. You should  comment  on their strengths  and weaknesses,  and  any improvements you would like to make to them in future.

Tool usage

Although you are free to use any tool, language, library that you like, there are some exceptions/conditions to this for you to be aware of.

In order to mark this assessment, I need to be able to see it! You absolutely *must* submit absolutely everything that is needed to create and view your assessment. This should include all code used to clean and filter data, any source data or intermediate datasets generated, and so on.

You  must  include enough instructions/information  to be able to run the code and reproduce the analysis/visualisations.

Tableau

If you use Tableau to create your visualisations, you *must* ensure you are either creating a single dashboard that combines multiple sub-visualisations together with some form of linked  functionality  between  the  sub-visualisations,  or  alternatively  an  effective  story presentation. Simply creating individual non-linked visualisations will not suffice.

If you use Tableau to create your visualisations you must submit a packaged tableau workbook that includes all needed resources within the packaged .twbx file2.

PowerBI

If you use PowerBI to create your visualisation, please make sure it is possible for someone to view your submitted visualisation. You must submit the .pbix file for your dashboard, and should also submit a link to the online version of the dashboard. As with Tableau, you *must*  ensure  you are creating a single   dashboard  that combines   multiple  sub- visualisations  together  with  some  form  of   linked  functionality   between  the  sub- visualisations

Python/JavaScript/…

Please submit a list of all libraries required to run your code/visualisations. This might be a pipfile or a requirements.txt for Python, or a package.json for javascript, and so on.

Java

No. No Java. It’s the only programming language that’s banned. I just can’t deal with the classpath issues.

Dataset Selection

You are free to choose data on any topic you like, with the following exceptions. You cannot use data connected to the following topics:

1.  COVID-19. I’ve seen too many dashboards of COVID-19 data that just replicate the work of either John Hopkins or the FT, and I’m tired of seeing bar chart races of COVID deaths, which are incredibly distasteful. Let’s not make entertainment out of a pandemic.

2.  World Happiness Index. Unless you are absolutely sure that you’ve found something REALLY INTERESTING that correlates with the world happiness index, I don’t want to see another scatterplot comparing GDP with happiness. It’s been done too many times.

3.  Stock Market data. It’stoodull. Treemaps of the FTSE100/Nasdaq/whatever index you like are going to be generally next to useless, candle charts are only useful if you’re a stock trader, and I don’t get a thrill from seeing the billions of dollars hoarded by corporations.

4.  Anything NFT/Crypto related. It’s a garbage pyramid scheme that is destroying the planet and will likely end up hurting a bunch of people who didn’t know any better.

Learning Outcomes Assessed

1.  Describe and discuss the theory behind visualisation design

2.  Critically analyse visualisations of data

3.  Examine and explore data to find the best way it can be visually represented 4.  Create static, animated and interactive visualisations of data

5.  Critically reflect upon and discuss the merits and shortcomings of their own visualisation work

Criteria for assessment

Credit will be awarded against the following criteria.