Coursework 1 – Data Visualization with Tableau (25%) Dataset NCD_RisC dataset on BMI (Body Mass index), Diabetes, and Blood Pressure; Available as the coursework attachment. Analysis goals:
1. Find a pattern for one of the measurements a. over time, and/or b. across different countries/regions
2. Find a relationship between two or three measurements a. over time and/or b. across different area Submission • One packaged Tableau workbook (.twbx file) with all the visualisations and the source dataset.
• A separate report (such as a word file) addressing all the marking criteria and explaining / providing justification of the work done
• A short (5min) presentation outlining the work done, results obtained and analytical insights Requirements
• Create two visualisations in Tableau, one for the single-measurement pattern (analysis 1), the other for the multi-measurement pattern (analysis 2).
• Each visualisation can be a work sheet, a dash board, or a story. o These need to be named Finding 1 and Finding 2, so they are clearly different from the result of tableau sheets.
• The findings need to be of different type. o These two findings are of the same type:
▪ The obesity level in UK increased from 2000 to 2010, and ▪ The percentage of population with raised blood pressure decreased from 2000 to 2010.
• Use Tableau Annotation to present/highlight the findings; • Use a separate report to address all the marking criteria Marking scheme (total 25%)
• Individual presentation carries 5% of the mark
• The report carries the remaining 20% of which there is a total of 10% mark for each finding split as indicated below: The findings (2%)
• What are the findings;
• The quality of the findings, i.e., how insightful is the finding; The 'what' (2%)
• What is Dataset type;
• What is the Data type;
• What is the Attribute type. The 'why' (2%)
• What does the visualisation aim to show
• Describe the ‘Actionsʼ from all three aspects: ‘Analyseʼ, ‘Searchʼ, and ‘Queryʼ; • Describe the ‘Targetsʼ from both the ‘Dataʼ and ‘Attributeʼ aspect The 'how' (4%)
• Describe the visual mapping or encoding (2%): What are the marks (point, line, shape, etc.); o What are the channels and what attributes are mapped to them (e.g., 'profit' is mapped to size); o Include features such as filtering and dashboard if there is any.
• Why are such visual mapping and design effective (2%): o Why is the chosen chart type a good fit for the finding (e.g., why bar chart is better than other chart types for this finding) o Is the visual mapping/encoding using the most effective visual channels? For example, why showing 'profit' with size is good for the finding? o This also applies to features such as filtering and dashboard if there is any.
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