Reference no: EM132864083

7BUIS009W Data Visualisation and Dashboarding - University of Westminster

Data Analysis, visualisation narrative and presentation

Learning Outcome 1: Discuss and critically apply the basic principles to data visualisation techniques;
Learning Outcome 2: select and justify appropriate tools for data visualisation and Dashboarding;
Learning Outcome 3: evaluate critically data visualisation using appropriate software tools;
Learning Outcome 4: use a design process to build interactive Dashboards;
Learning Outcome 5: synthesise the application of raw data into meaningful visualisation results and justify the appropriate techniques.

1. Formulate a research question - this will frame your analysis.
2. Carry out exploratory data analysis of the data using R or Tableau or both. You should make use of visual exploration of the data as part of your data analysis.
3. Using the findings from your analysis, construct a data graphics narrative to inform the viewer of the results of your data analysis and how you have interpreted them.
4. Refine the data visualisations produced so that they can be used in a short (no more than 5 minutes) presentation of the data story.
5. Write a short report explaining your process and critical thinking.
6. Present your data story using Panopto and submit a link to your presentation.

The written report:

A brief report of no more than 10 A4 pages (excluding cover sheets or appendices). No less than 12 point font and no less than 2cm page margins.

Any data graphics included in the text of the report will be considered outside of the minimum and maximum page limits. The data graphics may be included in a report appendix.

The report should cover the following areas:

a) The research question: A research question is a clear and concise question that summarises the issue your research will investigate. It should reflect something you are genuinely curious about.

b) Acquisition: Who created the data and why? How has the data been compiled How reliable can we expect the data to be? What are the analysis implications?

c) Preparation: Your treatment of the data in preparing it for analysis. You should cover the results of data integrity checks for data errors or missing data. You should also note any of the metadata or file formatting to enable analysis.

d) Discovery: Your exploratory data analysis work including a summary of your findings and methods used. Critically evaluate the methods used and why you used them. What was the most effective method? The least effective? Why?

e) Visualisations: What visual encodings did you consider? Which visual encoding did you choose and why? How have you used the software package tools and data visualisation theory to create and refine your final data graphics? Reference to published works on these topics would be appropriate here.

f) You should also discuss how you have improved your data visualisation using feedback from someone else who has not seen your graphics before. It is important to leave sufficient time to refine the data graphics so that they are as clear as possible - you will rarely get it right the very first time.

Narrative: The key points you want to make as a result of the data analysis work. What messages about the data do you want the viewer to walk away with after they have looked at your data graphics? What important trends have you found? Are there particularly unusual examples you should highlight?

Attachment:- Data Visualisation and Dashboarding.rar