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Cardiff School of Computer Science and Informatics

Coursework Assessment Pro-forma

Module Code: CMT218

Module Title:  Data Visualisation

Lecturer: Dr Martin Chorley

Assessment Title: Data Analysis and Visualisation Creation

Assessment Number: 2

Date Set: 7^th March 2023

Submission Date and Time: by 2^nd May 2023 at 9:30am

Feedback return date: 6^th June 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 60% 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 proceedings.

Assignment

You are asked to carry out 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 very short (2 page, ~800 words) reflective evaluation of the success (or not!) of your completed visualisation(s). This evaluation should critically discuss the visualisations you have created. 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.

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 file.

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’s too dull. 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.

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 linked below 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 21^st March at 5PM.  Any data redistribution as part of your submission must abide by the licence under which the data was obtained.

Dataset Selection form:

 https://forms.office.com/r/dYJpJrVM5y 

You will only be informed if there is a problem with your dataset selection, not if your selection is fine. If you have heard nothing by the date above, you can assume you are free to proceed with your chosen data.

Learning Outcomes Assessed

1. Examine and explore data to find the best way it can be visually represented

2. Create static, animated and interactive visualisations of data

3. 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.

Feedback and suggestion for future learning

Feedback on your coursework will address the above criteria. Feedback and marks will be returned on 6^th June 2023 via email, and marks and a link to feedback will be uploaded to Learning Central

Feedback from this assignment will be useful for your dissertation.

Submission Instructions

The coursework submission should consist of two items: an archive containing your visualisations, your code and data, and a PDF or word file containing your evaluation

Any deviation from the submission instructions above (including the number and types of files submitted) may result in a reduction in marks for that assessment or question part of 10%

Staff reserve the right to invite students to a meeting to discuss coursework submissions

Support for assessment

Questions about the assessment can be asked on https://stackoverflow.com/c/comsc/ and tagged with ‘cmt218-cw’, or at the beginning of the lectures in Weeks 8, 9 and 10.