CSD3335 Social Network Analysis and Visual Analytics Coursework 2 – Data Visualisation Design (20%) Tasks: use data visualisation to analyse a given dataset 1. Dataset: http://www.tableau.com/sites/default/files/training/global_superstore.zip (use all the sheets) 2. Analysis goal: is any part of the business (such as product type, region, or time of the year) losing money and why? Use the information in the spread sheet only. 3. Analyse your data exploration process, using the 'Actions' and 'Targets' from the Analysis Framework. 4. Design a visual story to report the findings of data exploration. Requirements  Data exploration and visual story should be created in Tableau.  The discussions on exploration process analysis and visual story design should be written as a report: o Refer to the lab exercises from Week 10 & 11 for examples. o The examples cannot be used as part of this coursework.  Submit the following files in a zip file in UniHub o A report that discuss the exploration process and story design. o A Tableau workbook containing the steps of the exploration process and visual story. Marking Scheme 1. Data exploration analysis (10%): discuss all the steps that led to one of the findings.  Minimal 5 steps: o Include every step during the exploration process, even those turned out to be dead ends; o Loading dataset is not a step in data exploration.  For each step, include the following information: o Screenshots of the visualisations, o The 'Action' and 'Target' of the analysis performed:  Describe the 'Actions' at all three levels: 'Analyse', 'Search', and 'Query'.  Describe the 'Targets' at the 'Data' and 'Attribute' level. o Any finding and how it is derived from the visualisation. o The logical connection between the steps: how the finding of a previous step leads to the next step in the exploration process. 2. Visual story design (10%)  The 'story' should have at least 3 visualisations; each can be a single chart or a dash board.  For each visualisation, o Include a screenshot and describe the findings it shows. o Discuss how the visualisation method matches the data/attribute type (such as categorical and ordinal) and the finding type (such as trend, outlier, and correlation). o Discuss any alternative visual designs considered and why they were not selected.