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Create two visualisations in Tableau, one for the single-measurement pattern (analysis 1), the other for the multi-measurement pattern
Coursework 1 – Data Visualisation with Tableau (20%) Dataset NCD_RisC dataset on BMI (Body Mass index), Diabetes, and Blood Pressure; Available as the coursework attachment.
Analysis goals: . Find a pattern for one of the measurements over time, and/or
across different countries/regions
. Find a relationship between two or three measurements over time and/or
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 (see group work requirements for example).
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.
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. These two findings are of the same type: T he o be s i ty le v el i n U K i n c r ea s ed f ro m 2000 to 2010 , and
T he p e r ce nt age o f popu la t i on w i t h r ai s ed bl oo d pr e ssur e dec r ea s ed f ro m 2000 to 2010 .
Use Tableau Annotation to present/highlight the findings;
Use a separate report to address all the marking criteria (see group work requirements for example).
Marking scheme (total 20%) There is a total of 10% mark for each finding:
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.);
What are the channels and what attributes are mapped to them (e.g., `profit` is mapped to size);
Include features such as filtering and dashboard if there is any.
Why are such visual mapping and design effective (2%):
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)
Is the visual mapping/encoding using the most effective visual channels? For example, why showing `profit` with size is good for the finding?
This also applies to features such as filtering and dashboard if there is any.
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