Visual Analytics Resit Coursework Specification
Summer June 2025
1. Overview
The goal of this coursework is to give you experience of the whole lifecycle of carrying out a full visual analytics project.
Your goals are:
· To follow a sound visual analytics process
· To develop a visualisation that displays important features of a dataset
· To write a clear report on your findings.
The outputs from this work should be
1. a Tableau dashboard and associate worksheets (as a packaged workbook);
2. a written report with sections as defined below.
The submission deadline is 13:00 on the 21st of July: create a single zip file for all the files in your submission and submit to Blackboard,
2. Task Details
The task you are asked to carry out for the coursework is to design, construct, and evaluate an exploratory analysis of a dataset using visualisation and data projection.
The data you should work with is taken from the 1994 census in the USA which can be found here https://archive.ics.uci.edu/ml/datasets/Adult. You should not use more than 3-4 tables.
You must provide at least two data projections using different algorithms. You are also expected to use Bayesian methods appropriately.
Your report should contain the following sections:
· Abstract. A brief description of the key points in the report.
· Introduction. The background of the problem.
· Data Preparation and Abstraction. What data manipulation was necessary to create a dataset for analysis and the principal data types and semantics that you have analysed.
· Task Definition. A description of the tasks for which you have created the visualisations.
· Visualisation. Justification. Which visualization techniques you used and a justification for your choices. This justification and explanation are a very important assessment criterion, so do not skimp on this and make sure that it is grounded in the theoretical concepts we have covered during the course. You should refer to the principles of info vis, relevant aspects of human perception and cognition, and the scientific literature where appropriate.
· Conclusion.
- What you have learned about the socio-economic problem that was the basis of the visualization.
- What you have learned about information visualisation from doing the coursework.
I am expecting the report to be about six pages in length. This is an expectation, not a strict limit, so there will be no penalty for exceeding it. But if you find yourself writing much more than this, you are almost certainly providing too much detail. In particular, note that I will see the visualisation you generate, so there should be little or no need for screenshots.
The assessment criteria are:
· Problem understanding: how well you have explained the goals of the tasks, taking account of end-user requirements. (10 marks)
· Data preparation and task analysis: care taken over extracting and manipulating the data; insights gained through the task analysis. (10 marks)
· Data visualisation: appropriateness of visualization and modelling approaches; systematic use of statistical and visualisation methods; justification of visualization approach used. (55 marks)
· Conclusions: effectiveness and insight of the evaluation; what the user should learn from your analysis. (15 marks)
· Presentation: fluency and coherence of the written text; quality of images and graphics used. (10 marks)
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Please consider the general feedback that was given on a similar coursework last year.
· Ensure that questions you set out to ask are answered by the visualisation and in the report.
· Having the option of switching between absolute values and proportions is often a useful feature. This is particularly helpful when comparing areas with different populations.
· When using dimensionality reduction it is important to communicate to the user which variables were used in the original data space as otherwise it is hard to interpret the plots.
· Tooltips should identify the corresponding point (e.g. a location) particularly for projected data.
· The introduction should contain some discussion of the type of user the visualization is intended for.
· The report should note data anomalies (e.g. missing values) in report, in particular, quantifying the number of missing values etc.
· The abstract should describe the main findings of the work.
· Data cleaning matters.
· The use of section and page numbers helps the reader to navigate the report.
· References to secondary literature are valuable to provide context.