CS5803 Data Visualisation assessment Coursework for 2025/26 Brunel University london

CS5803 Data Visualisation

TABLE OF CONTENTS

Main Objective of the Assessment 
Description of the Assessment
Learning Outcomes and Marking Criteria
Format of the Assessment
Submission Instructions
Avoiding Academic Misconduct
Expectations of Artificial intelligence Use
Late Coursework

FORMAT OF THE ASSESSMENT

This submission will comprise your final report (in both PDF and Word file formats) along with all files required to run your implementation. The PDF will form the main submission with all other files collated into a single archived file (zip or 7z format only) provided as an attachment to the main submission. The report should be prepared as a MS Word file (doc or docx) which should included in the zip file in its original format. The Word and PDF versions of the report should be identical – it is recommended that you create the PDF from within the Word application.

Both the Tableau project file (e.g. twb or twbx) and all data files required to load and run this project should be included in the archived appendix file. If you have created a second implementation, all required files should also be included in the appendix file. It is strongly recommended to save your Tableau project in packaged (twbx) format, as this significantly reduces the likelihood of problems (e.g. missing data or broken file paths) when loading files on another PC. Prior to submission, you are advised to test your zip file by extracting to a different PC with Tableau installed (e.g. a lab PC) and checking that your project opens and runs as expected. The marker will always try their best, but if they are unable to run your solution without errors your work is likely to fail on learning outcome one.

Summarising from the task description above, the final report should contain this section structure:

• Introduction: A description of the dataset (including source URL) and data dictionary for all variables used, user persona, planned questions and relevant requirements (use the format provided in the Design Exercise in Week 25):

• Data
• Persona and questions
• Requirements

• Design: You should include both the original prototype design sketch (in Paper Landscape format) and an annotated screenshot of the final implementation. Any changes made during implementation should be clearly highlighted and explained here.
• Implementation: Describe the steps followed to implement your design using Tableau. This need not be an exhaustive click-by-click tutorial but should be sufficiently detailed to allow a competent user to replicate your solution. If you did a second implementation, provide a brief description (<200 words) that focuses on key differences in the implementation process and outcome.
  
  
• Walkthrough: This should be a clear demonstration of the process you followed to arrive at your final answers and, importantly, what those answers were. This should be illustrated in the report using screenshots from your Tableau Story sheet.
  
  
• Discussion (optional): a discussion that critically evaluates the project as a whole including reflections on your personal learning (during the module and looking forwards) and the relative strengths and limitations of the visualisation tool(s) used.
  
  
• Conclusion: a concise, single paragraph summary of the project objectives (data and set questions), the design choices you made, and the key insights gained from your visualisation.
  
  
• References, a list of sources that you cited throughout the report. This should be presented in Harvard format. Any use of generative AI should also be declared here, including the name of the tool and the prompt(s) used.

Use single line spacing and font size of 10. You are free to add sub-sections within these sections, to improve readability, but do not omit, rename, or change the order of these sections. The main body (excluding cover page and references) of the report should contain no more than 3000 words and 20 pages in length. It is important that you adhere to these limits as any significant excess will result in failure on one of LO2 criteria.

Note that your work will be assessed according to the marking scheme by applying the C grade criteria first, followed by the B criteria and then the A criteria. If you fail any of the C grade criteria you will fail the assessment, regardless of whether your work meets any higher criteria. Therefore, please make sure every C grade criterion has been met before submitting your work.

SUBMISSION INSTRUCTIONS

You must submit your final report on Wiseflow before 11:00 (GMT/BST) on the indicated date.  You can follow the link to Wiseflow through the module’s section on Blackboard Learn or login in directly at https://uk.wiseflow.net/brunel.

Remember, you need to submit two files to Wiseflow for your final report. The first is a PDF of your report document. The second is a Zip file containing your Word file all and all supporting project and data files which you will submit as an appendix. The name of both files should contain your student ID number and, optionally, the module code. For instance, you might submit your appendix file as 2512345.zip or CS5803_252345.zip.

AVOIDING ACADEMIC MISCONDUCT

Before working on and then submitting your coursework, please ensure that you understand the meaning of plagiarism, collusion, and cheating (including contract cheating) and the seriousness of these offences.  Academic misconduct is serious and being found guilty of it results in penalties that can reduce the class of your degree and may lead to you being expelled from the University.  Information on what constitutes academic misconduct and the potential consequences for students can be found in Senate Regulation 6.
You may also find it useful to read this PowerPoint presentation which explains, in plain English, the different kinds of misconduct, how to avoid (even accidently) committing them, how we detect misconduct, and the common reasons that students give for engaging in such activities.  
If you are experiencing difficulties with any part of your studies, remember there is always help available:

• Speak to your personal tutor.  If you’re not sure who your tutor is, you can find the information on eVision or you can ask at the Student Hub (studenthub@brunel.ac.uk).
  
  
• The Student Hub can also provide advice on support and welfare issues.

EXPECTATIONS OF ARTIFICIAL INTELLIGENCE USE

The University has general guidance on using artificial intelligence in your studies. Whilst AI tools (like ChatGPT or Gemini) may be used for early, formative research and learning, under no circumstances should any AI generated output be pasted unmodified and unattributed into coursework submissions. In other words, AI derived content should be used selectively and appropriately cited in the same way as any other secondary source.

LATE COURSEWORK

The clear expectation is that you will submit your coursework by the submission deadline stated in the study guide. In line with the University’s Coursework Submission Policy (revised in January 2025), coursework submitted up to 48 hours late will be accepted but the penalties set out in the ‘Late Submission’ section of the policy will be applied. Work submitted over 48 hours after the stated deadline will automatically be graded NS (non-submission).

Please refer to the Computer Science student information pages and the Coursework Submission Procedure pages for information on submitting late work, penalties applied and procedures in the case of Extenuating circumstances.