代写CMT218 Data Visualisation代写留学生Java程序

日期：2024-04-29 04:03

Module Code: CMT218

Module Title: Data Visualisation

Assessment Title: Data Analysis and Visualisation Creation

Assessment Number: 2

Date Set: 4^th March 2024 

Submission Date and Time: by 2^nd May 2024 at 9:30am

Feedback return date: 30^th May 2024

If you have been granted an extension for Extenuating Circumstances, then the submission deadline and return date will be later than that stated above. You will be advised of your revised submission deadline when/if your extension is approved.

If you defer an Autumn or Spring semester assessment, you may fail a module and have to resit the failed or deferred components.

If you have been granted a deferral for Extenuating Circumstances, then you will be assessed in the next scheduled assessment period in which assessment for this module is carried out.  

If you have deferred an Autumn or Spring assessment and are eligible to undertake summer resits, you will complete the deferred assessment in the summer resit period.  

If you are required to repeat the year or have deferred an assessment in the resit period, you will complete the assessment in the next academic year.

As a general rule, students can only resit 60 failed credits in the summer assessment period (see section 3.4 of the academic regulations).  Those with more than 60 failed credits (and no more than 100 credits for undergraduate programmes and 105 credits for postgraduate programmes) will be required to repeat the year.  There are some exceptions to this rule and they are applied on a case-by-case basis at the exam board.

If you are an MSc student, please note that deferring assessments may impact the start date of your dissertation.  This is because you must pass all taught modules before you can begin your dissertation.  If you are an overseas student, any delay may have consequences for your visa, especially if it is your intention to apply for a post study work visa after the completion of your programme.

NOTE: The summer resit period is short and support from staff will be minimal. Therefore, if the number of assessments is high, this can be an intense period of work.

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 and academic regulations can be found on the Student Intranet:

https://intranet.cardiff.ac.uk/students/study/exams-and-assessment/extenuating-circumstances

https://intranet.cardiff.ac.uk/students/study/your-rights-and-responsibilities/academic-regulations

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 declare that I have not made unauthorised use of AI chatbots or tools to complete this work, except where permitted.  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 still working for the excellent and very real website ‘martins-cool-datavis-website’ as a key contributor. Your last contribution to the website was received very well, and the editorial team would like you to take on a more involved task. They would like to produce a feature on the website where a data visualisation practitioner creates a piece of data visualisation and then explains *why* they have created the visualisation that way, to show the process that goes in to developing a successful data visualisation.

To do this, you are first asked to carry out an analysis of a dataset and to identify:

1. What key message can you communicate about this data through a data visualisation?

2. Who are the audience that would be interested in a data visualisation on this topic?

You should then create a maximum of two (2) visualisations, (or a single (1) dashboard comprising a set of linked sub-visualisations) communicating the key message about the dataset you have selected to the audience you have identified.

Alongside this data visualisation you should write a very short (2 page, 800 word) article evaluating why the visualisation you have created is a good visualisation to communicate the key finding from your data analysis to your chosen audience. This evaluation should not focus on *what* the data says, or *what* you are trying to communicate, but rather *why* your visualisations do a good job of communicating the information that you have found out about the data. It must *not* just be a description of what the visualisations show and what they tell us about the data.

The data you choose to analyse should be 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 and data used must be included alongside the article, so that readers can reproduce your visualisations. You should include a small appendix to the article that must include enough instructions/information to be able to run the code and reproduce the analysis and/or visualisations.

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. The editorial team for the website want to ensure the piece you are creating is accessible to the widest audience possible.

In order to view this piece, the audience need to be able to see it! You absolutely *must* submit everything that is needed to create and view your visualisation. This should include all code used to clean and filter data, any data required, 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/R…

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. We 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. We’ve seen too many dashboards of COVID-19 data that just replicate the work of either John Hopkins or the FT, and we’re 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, we 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 we don’t enjoy 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 contributor to the website 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 contributor may ‘claim’ the dataset first!  Data selection should be completed by 19^th 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

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.

Component & Contribution	Fail (<50)	Pass (50-59)	Merit (60-69)	Distinction (70+)
Visualisation Presentation (60%)	None/poor visualisation of data with fundamental errors present. Poor data presentation No message conveyed to user, message/findings unclear No consideration of audience	Rudimentary or basic visualisation of data Message/story partly clear to end user. Some consideration of audience	Appropriate visualisations that may require some polish or editing to reach a professional level. Message/story clearly communicated to an identified audience.	Appropriate visualisations that clearly communicate to the chosen audience and are of a professional level. Message/story clear
Visualisation Evaluation and Discussion (40%)	Little to no evaluation, is essentially a description of what the visualisation is/are and/or what it/they shows	Some effort at evaluation, but still quite descriptive of what is shown, rather than why the visualisation has been created in this way	Reasonable evaluation, justifying many of the decisions made in creation of the visualisations and relating these to principles discussed in the module	Insightful evaluation, fully considers the good and bad points of the visualisation, improvements that could be made, and explores the trade-offs made in the visualisation process

Feedback and suggestion for future learning

Feedback on your coursework will address the above criteria. Feedback and marks will be returned on 30^th May 2024 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