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Assignment Briefs 11-16-2022

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: 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 (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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