代写CMT218 Data Visualisation代做Java编程

日期：2024-05-07 03:27

Coursework Assessment Pro-forma

Module Code: CMT218

Module Title:  Data Visualisation

Assessment Title: Data Visualisation Resit

Assessment Number: RESIT

Date Set: 15^th March 2024

Submission Date and Time: by 17^th May 2024 at 9:30am

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 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 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 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 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 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.

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.

Component & Contribution	Fail (<50)	Pass (50-59)	Merit (60-69)	Distinction (70+)
Visualisation and Data Presentation (60%)	None/poor visualisation of data with fundamental errors present. Poor data presentation No story conveyed to user, story/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 (40%)	Little to no evaluation, is essentially a description of what the visualisation is/are and/or what it/they shows   No reference to visualisation principles No understanding of context and no evidence of further reading Errors in reasoning/argument	Some effort at evaluation, but still quite descriptive of what is shown, rather than why the visualisation has been created in this way Logical argument Understanding of relevant visualisation principles Evidence of further study Structured and consistent presentation	Reasonable evaluation, justifying many of the decisions made in creation of the visualisations and relating these to principles discussed in the module Critical insights and evidence of critical thinking Application of relevant principles Good use of further sources Well organised and structured	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 Sophisticated and intelligent argument. Excellent understanding and application of principles Evidence of original thinking Well-chosen sources used to strengthen argument Excellent structure and organisation

Feedback and suggestion for future learning

Feedback on your coursework will address the above criteria.

Feedback from this assignment will be useful for your dissertation.

Submission Instructions

Coursework must be submitted via email to [email protected]

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